Agriculture, in particular, has drawn a lot of attention lately due to the introduction of innovations like machine learning and smart computing. It is becoming increasingly challenging for farmers to effectively manage land and optimize profit in a particular terrain due to the changing economics of agri-produce. Crop yield forecast is heavily reliant on environmental parameters such soil composition, rainfall, humidity, and cultivable area, among other crucial indicators. Because they don’t adequately account for a variety of environmental factors, traditional Crop Yield Prediction approaches like historical averages frequently don’t yield reliable results. Furthermore, farmers find it challenging to choose crops and cultivate them effectively due to shifting market patterns in supply and demand. While a shortage of a certain crop could result in lost profit chances, a surplus production could result in reduced market pricing. Thus, combining yield prediction models with demand and supply research can assist farmers in improving crop planning for increased profitability. These challenges are addressed and accurate forecasts are generated using a machine learning-based approach. Crop prediction is done with classification models, whereas yield prediction is done with regression models trained on both historical and present data. To identify best course actions, these models examine a number of performance indicators. For practical use, the top-performing model is integrated into the backend. With a MAE of.64, an R-squared mark of.96, Random Forest Regression outperforms the other models employed for yield prediction. At 99.39%, the Naïve Bayes classifier has the best accuracy for crop prediction. Predictions are further improved by adding market data to these models, such as price swings, customer demand, and past sales patterns. Farmers can improve profitability and minimize waste by matching their agricultural techniques with market demands through the integration of demand and supply analytics. This study demonstrates how machine learning may transform crop management by assisting farmers in making data-driven decisions to match their output with supply and demand in the market, as well as by optimizing resource allocation and raising total yield.