A data-informed system is described for generating crop recommendations and crop yield forecast based on a variety of data sources of farmer-level soil characteristics, historical crop yield records, and meteorological variable data. In the proposed system, crop recommendations based on a classification algorithm and crop yield estimates based on a regression algorithm are provided to farmers. The data-driven crop recommendations and crop yield forecasts will improve decision-making by providing the farmer with data-based recommendations providing the productivity isolation. The data-informed system will utilize machine learning algorithms to process the data and analyze the complex interaction of the various farming agri-parameters in the farm operation. Composition of soil nutrient values, weather patterns, and historical productivity variable data will be a key ingredient in the model to provide farmers with singularly specific crop selections. Ability to yield prediction gives farmers anticipate yield of the crops, improve resource planning. The validation tests demonstrate better accuracy than traditional heuristics, improving farmer overall risk reliability and increasing efficiency, sustainability. The results shows us that the transformative role of machine learning in agriculture and the associated movement toward precision farming practices.