AI with data engineering pipelines as an innovative way has become relevant in the context of optimizing the real-time decision-making systems. The objective of this research is to explore the potential of AI models in making decision support more efficient when integrated into today’s data science platforms. The overarching goal is to provide an ability to fill the gap between data collection, analysis and decision-making through use of intelligent systems, which adapt to environments and continually learn.

To this end, the study uses a mix of architectural assessment, a prototypical implementation, and case studies. State-of-art technologies like machine learning and deep learning are combined with data engineering technologies like Apache Kafka, Spark, Kubernetes for real time data processing and management. It involves emulating high velocity data streams and applying AI’s analytics to it what would be accomplished in real time.

The results show increased adaptability, speed and effectiveness in decision-making systems. By directly integrating AI into an organization’s data streams, it is possible to decrease the time taken to process the data by half, and get a more accurate analysis by 30%. Real-world applications of the technique involve fraud detection in financial services and improved supply chain management; performance of the method in larger organizational contexts is also demonstrated.

The implication from this research is that integration of AI with data engineering pipelines is not only possible but imperative especially to organizations that are willing to step up their game and competitiveness in the current era characterised by an influx in data generation. Subsequent work will investigate how new technologies like federated learning and edge computing can be applied progressively to boost the effectiveness of real-time decision making frameworks.

The main objective of the study was to identify the major threats of African elephant in Kafta-Shiraro National Park, Tigray, Ethiopia. The data was collected using questionnaires, interview, discussion with key informants and direct field observation. Currently Kafta-Shiraro National Park was affected by many threats such as Agricultural encroachments (1^st), Traditional mining extraction (2^nd), Deforestation (3^rd), Charcoal production (4^th), Irrigation activity (5^th), Fire (6^th) and Illegal hunting and poaching (7^th) ranking based on the field observation and questionnaire from respondents. Among the main sources of conflict between human–African elephant in the national park were crop damage (57%), competition for resources (19%), necessity of guarding field (12%), destruction of property (8%) and people killed by elephant (4%).  Habitat disturbance, livestock interference, feed shortage and illegal hunting were the main threats of African elephant in the park. Poor community awareness, high population, frees access for resources, weak law enforcement and poor patrolling were the major problems for effective management of elephants in the park. After analyzed the data African elephant conservation training were given for 40 community representatives for 6 consecutive days. Finally the office of the national park should work in collaboration with the local community in order to solve the current threats of the park to sustainable resources for the next generation.

Rangeland degradation remains a serious impediment to improve pastoral livelihoods in the lowlands of Ethiopia. This review paper presents an overview of the extent of rangeland degradation, explores its drivers, discusses the potential impacts of rangeland degradation and also suggests alternative rangeland restoration techniques. It is intended to serve as an exploratory tool for ensuing more detailed quantitative analyses to support policy and investment programs to address rangeland degradation in Ethiopia. The extent of rangeland degradation increases with time, and the productivity of rangelands are losing if not given due attention. The major drivers leading to rangeland degradation includes climate change, overgrazing, bush encroachment, population pressure, drought, and government policy, encroachment of rain fed agriculture and decline of traditional resource management institution. Degradation of rangeland has resulted in substantial declines in rangeland condition, water potential, soil status, and animal performance, livestock holding at the household level and community become destitute. Another consequence of rangeland degradation is linked to food insecurity, poverty to the extent of food aid, expansion of aridity and the need for alternative livelihood and income diversification. Moreover, it has increasingly become a threat to the pastoral production systems, and has contributed towards increases in poverty and tribal conflicts over grazing land and water resources. In spite of these impacts, the adoption of alternative restoration techniques in the country is highly insufficient. To address rangeland degradation problems, there is a strong need to substantially increase the investments and strengthen the policy support for sustainable land management.

Protected areas are managed for a variety of reasons, including the conservation of species and ecosystems, the preservation of landscapes, the protection of watersheds, and the preservation of important biological reserves, and, increasingly, the sustainable use of natural resources by local people. Although protected areas safeguard many of the world's environments and species, human encroachment is seriously deteriorating and destroying many of these regions, particularly in the tropics, including Ethiopia. Protected places may have a good or negative impact on the local society and economy. Direct revenue from environmental conservation and the maintenance of ecosystem services such as watershed protection are examples of good local community impacts. The negative consequences might range from relocation of local residents to wildlife damage to crops, as well as restricted resource availability and changes in land tenure. Individual protected areas, organizations, and countries, as well as their management categories and forms of governance, differ substantially in terms of protected area management and community involvement. All of these topics are covered in this work.

The increasing use of artificial intelligence (AI) applications in cloud and edge environments raises significant concerns regarding security, data integrity, and the trustworthiness of models. Traditional security measures often struggle to provide adequate protection against tampering, unauthorized access, and privacy breaches. However, blockchain technology—with its decentralized architecture, immutability, and transparency—offers a promising solution to these challenges. This research explores the integration of blockchain in the development of AI for cloud and edge environments, emphasizing its potential to secure data provenance, protect AI models, and enable privacy-preserving learning. By analyzing use cases such as federated learning, decentralized AI marketplaces, and edge device security, this study provides insights into the opportunities and challenges presented by this convergence. The proposed framework highlights scalable blockchain solutions that align with the performance requirements of modern AI systems, offering a pathway for secure and trustworthy AI development in distributed settings