Agriculture is the backbone of the Indian economy, and with the advent of cutting-edge technologies like deep learning (DL) and machine learning (ML), there is a lot of potential to transform traditional farming practices into sophisticated, data-driven operations. This project offers a state-of-the-art web-based platform that integrates three primary applications—crop recommendation, fertilizer suggestion, and plant disease detection—to assist farmers in optimizing their agricultural operations. The Plant Disease Detection module allows users to upload images of ill plant leaves. Using ResNet, a deep convolutional neural network trained on large datasets of diseased and healthy leaves, this tool accurately identifies diseases and provides useful treatment recommendations. By automating the diagnosing procedure, this tool assists farmers in proactively managing plant health, reducing crop loss, and increasing yields. The Fertilizer Recommendation tool makes suggestions based on specific crop and soil characteristics. By looking at important soil parameters including pH, moisture, nitrogen, phosphorus, and potassium (NPK levels), the program suggests the ideal fertilizers and dosages for different crops. This precision-based approach lessens waste and its negative effects on the environment while promoting sustainable farming practices and soil health. The Crop Recommendation tool assists farmers in choosing suitable crops by utilizing soil data, environmental factors, and location-specific information. The system uses algorithms like Decision Trees and Support Vector Machines to analyze soil properties, seasonal weather data, and regional variables to recommend crops with the best growth potential. This improves agricultural output, reduces the risk of selecting incompatible crops, and empowers farmers to make informed decisions.