Krishi Mitra - Intelligent Crop And Fertilizer Recommender System employs machine learning algorithms to provide personalized crop and fertilizer suggestions based on specific farming conditions, including various environmental parameters such as temperature, humidity, pH, rainfall, crop types, and soil nutrient (N,K,P) concentrations. The proposed research aims to enhance crop recommendations for farmers by predicting the most suitable crops for their unique agricultural settings. To enhance overall prediction accuracy, the crop recommendation model leverages an Ensemble approach, where an ensemble model is trained using Random Forest and XGBoost algorithms, and its performance is compared against that of the Artificial Neural Networks (ANN) algorithm. This comparative analysis allows for the selection of the most accurate model for crop recommendation and fertilizer customization. This approach optimizes crop selection and fertilizer recommendations, potentially leading to increased crop yields and improved farming productivity. The proposed implementation of this system holds the potential in revolutionizing agriculture by offering sustainable recommendations, bridging the gap between farmers and technology, and enhancing agricultural productivity and sustainability.