Artificial Intelligence and Data Science for Sustainable Agriculture: A Comprehensive Analysis of Technologies, Applications, and Implementation Challenges

The transformative potential of Artificial Intelligence (AI) and Data Science (DS) in modern agriculture is increasingly recognized as a solution to global challenges of food security, resource optimization, and sustainable farming. This paper presents a comprehensive analysis of AI and Data Science technologies such as Fuzzy Logic, Artificial Neural Networks (ANN), Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Artificial Potential Field (APF), Simulated Annealing (SA), and data-driven predictive models applied in agriculture. The study highlights how AI–Data Science integration empowers precision and smart agriculture through innovations like automated irrigation, crop yield prediction, pest and disease detection, autonomous farm equipment, and data-enabled supply chain optimization. It further explores how these technologies enhance productivity and sustainability while minimizing environmental impact. The paper identifies key implementation challenges—including data quality, model interpretability, and adoption barriers—and recommends future research in explainable AI, hybrid intelligent systems, and climate-adaptive solutions. This review underscores the synergistic role of AI and Data Science in driving the next generation of sustainable agricultural innovation and serves as a valuable resource for researchers and practitioners aiming to advance smart and resilient farming systems.