Maharashtra is a leading State in agriculture. Agriculture is the one that plays important role in the economy of India. India is an agricultural country and its economy largely based upon crop production. Hence one must say that agriculture is often the backbone of all businesses in the a-part-of-us country. Basically paper focuses on predicting the yield of the crop by using a different machine learning algorithm. The application (Smart Farm) developed in this research helps users to predict the crop yield using different climatic parameters. Different methods of predicting crop yield are developed within several years with different outcomes of success, but many of them do not take into consideration the climate. Machine Learning is the best technique which gives a better practical solution to crop yield problem. So the Random Forest algorithm which we decided to used to train our model to give high accuracy and best prediction., we chose 5 climatic parameters to train the model. Agriculture inputs such as pesticides, fertilizers, chemicals, soil quality, etc. were not used as it depends upon the type of field. The model is trained and designed using 20 decision trees build the random forest algorithm which gives better accuracy of the model. 10-fold cross-validation technique used to improve the accuracy of the model. The predicted accuracy of the model is analyzed 87%.