In recent years, there has been an increasing interest in the application of precision agriculture techniques to improve crop health management, with AI, data science, and advanced sensor technologies playing a vital role in this transformative process. The traditional overreliance on broad-spectrum chemical pesticides is critically threatened by environmental degradation, pest resistance, and socio-economic inequities. The combination of remote sensing, IoT networks, machine learning, robotics and autonomous systems makes precision approaches possible for early detection, real-time monitoring and targeted interventions. Case studies illustrate successful pursuits in areas like reduced fungicide use in the vineyards of France and AI-driven management of fall armyworm outbreaks in Sub-Saharan Africa. Artificial Intelligence (AI) based DSS (Decision Support Systems) integrate and interpret diverse data streams to generate actionable insights for decision-making processes, and drones and ground robots allow infrastructure bound for site-specific treatments, supporting the reduced utilization of chemicals and lowering environmental hazards. However, technical barriers remain—such as data interoperability, sensor reliability, and computational demands—as well as ethical issues and policy gaps. The chapter underlines the importance of interdisciplinary collaboration to address socio-economic disparities and facilitate scalable adoption. New technologies like quantum computing, CRISPR-based crop engineering, and edge computing provide better predictive analytics and operational efficiency. Precision agriculture would be a solution to mitigate climate-driven pest dynamics and ensure food security globally by integrating the best in class innovation while maintaining ecological and economic sustainability.