Recent developments in artificial intelligence and machine learning have introduced new approaches to agriculture, including optimizing crop yield and resource utilization. This paper presents KrishiTech, an integrated platform using machine learning models to address two key agricultural management challenges: predicting wheat production and recommending optimal crops based on environmental conditions. The first uses an XGBoost Regressor to model the production of wheat, accompanied by yield and environmental factors coupled with land areas to achieve R2 of up to 0.9929 which produces nearly flawless predictions that assist with policy determination and resource selection. The random forest classifier identifies ideal crops due to soil-based variables such as nitrate levels, phosphorus and potassium levels of temperature, relative humidity, the pH, as well as average rainfalls—this model comes at a perfect 99.55% rating of classification results. Both the models were validated with real data, which looks promising. KrishiTech is a huge leap toward the digitalization of agriculture, decision-making, and sustainability in farming by predictive analytics and data-driven insights.