The agriculture sector is the primary occupation for the majority of people in India, with over 70% of the population engaged in agricultural activities. It serves as the backbone of the country’s economic development and the lifeline for most of its people. However, improper farming practices have led to declining agricultural productivity, making it essential to determine ideal crops for specific seasons and ensure the proper application of organic and inorganic fertilizers. Additionally, early detection of plant diseases and accurate crop yield prediction are crucial for maximizing productivity. Choosing appropriate crops involves analyzing multiple factors such as climate, rainfall, and soil NPK content. A reliable fertilizer recommendation system is necessary to provide the right nutrients for optimal crop growth, while an effective plant disease detection mechanism helps prevent crop loss. A strong machine learning model is needed to support precision farming, incorporating four major components: crop recommendation, fertilizer suggestion, plant disease detection, and crop yield prediction. Using Random Forest, the system achieved 99.54% accuracy for crop recommendation, 98.73% accuracy for fertilizer suggestion, and an R2 value of 0.84 for crop yield prediction. For plant disease detection, ResNet-9 achieved 99% accuracy. This framework optimizes agricultural practices, ensuring better productivity and sustainability.