Agriculture plays a vital role in India's economy, employing a significant portion of the population and ensuring food security. However, challenges such as climate change, resource scarcity, and increasing food demand necessitate innovative solutions for optimizing crop production. This research investigates the impact of environmental factors—temperature, humidity, and rainfall—and agronomic variables across key phenological stages, including germination, tillering, panicle initiation, flowering, and maturity. Advanced machine learning models, such as Random Forest, Lasso Regression, and Principal Component Regression (PCR), are utilized alongside exploratory data analysis and correlation studies to evaluate interactions among variables like fertilizers, Growth Degree Days (GDD), and epigenetic markers (SPAD values and leaf area index). Findings highlight the significance of phosphorus, potassium, and environmental factors such as rainfall and humidity in determining crop yields, with their relative importance varying across stages. Principal Component Regression demonstrated superior predictive accuracy (R2 = 99.53 and MAE = 2.06), underscoring its potential in precision agriculture. By providing data-driven insights into irrigation, fertilization, and pest management, this study offers practical recommendations to enhance crop productivity under diverse agronomic and climatic conditions. The results emphasize the critical role of nutrient optimization, genetic trait selection, and adaptive strategies in achieving sustainable agricultural practices.