This paper presents an innovative IoT-based autonomous farming system utilizing machine learning models to assist farmers in determining suitable crops based on real-time environmental data. The system integrates IoT sensors, including soil pH, NPK, temperature, and humidity sensors, to collect data from the field. The ESP8266 NodeMCU processes this data and transmits it to a cloud database. A range of machine learning algorithms were applied to the dataset, including Logistic Regression, Gaussian Naive Bayes, Support Vector Classifier, K-Nearest Neighbors, Decision Tree Classifier, Extra Trees Classifier, Random Forest, Bagging Classifier, Gradient Boosting, and AdaBoost. The highest accuracy was achieved with the Random Forest Classifier (97.05%), followed closely by the Bagging Classifier (96.59%) and Gradient Boosting (96.36%). The AdaBoost model showed poor performance with an accuracy of 10.23%. The system’s predictions are accessible to farmers via a web or mobile application, enabling them to make informed decisions about crop cultivation. This IoT and machine learning-based approach reduces human intervention, optimizes farming practices, and enhances crop yield potential. The system provides real-time crop recommendations, making farming more efficient and sustainable. The use of appropriate algorithms on the sensed data can help in the recommendation of suitable crops.