Climate change has significantly impacted global agricultural productivity, leading to increased unpredictability in weather patterns. Artificial Intelligence (AI) has emerged as a powerful tool for enhancing weather prediction models, enabling farmers to make informed decisions. This paper explores AI-driven weather prediction models and their role in climate-resilient agriculture. It discusses traditional meteorological techniques, AI methodologies, and the integration of AI in precision farming. Additionally, it highlights the challenges and future prospects of AI in sustainable agriculture. Furthermore, the study explores the role of Database Management Systems (DBMS), Data Warehousing, and Data Mining in optimizing AI-driven weather forecasting for agriculture.
AI, Machine Learning, Weather Prediction, Agriculture, Climate Resilience, Precision Farming, Deep Learning, Data Warehousing, Data Mining, DBMS