This book explores the intersection of Machine Learning (ML), Artificial Intelligence (AI), and agriculture, focusing on enhancing farming practices through data-driven solutions. It begins with an evaluation of fertilization and irrigation systems, addressing integration challenges and essential components like sensors, communication interfaces, and fertilization mechanisms. Book highlights the difficulty in selecting appropriate models due to the abundance of options, leading to delays and higher costs. To address this, it compares fertilization and irrigation models based on performance metrics such as accuracy, cost, complexity, and scalability. It also proposes enhancements like model fusion to improve system performance and reduce validation efforts. The thesis introduces the "MSMRBEF" framework for soil monitoring, using bioinspired ensemble processing and genetic algorithms to recommend crops based on environmental conditions. The "LEIFMCY" model, a low-cost, IoT-based solution for cotton yield analysis, is presented, optimizing crop yields through real-time soil monitoring and predictive analysis.