Agriculture is India's most essential activity since it serves people's food demands and the raw material needs of countless enterprises. Innovative agricultural approaches gradually increase crop output, increase farm profitability, and decrease irrigation waste. A dependable remote monitoring solution is essential. Two goals are covered in this study. First, a survey on IOT based agricultural model and its algorithms. Second, a comparative analysis of various authors proposed solutions with accuracy. This review paper is a machine learning-based IOT agricultural data analytics. In agriculture plant is heavily influenced by three factors: moisture, temperature, and relative humidity. An IoT device with cloud capabilities is created from data gathered by sensors deployed in a farm's field and sent to a microprocessor or Arduino. The DT approach is a powerful machine-learning tool for predicting outcomes from field-sense data. Farmers are provided the findings of the decision tree algorithm to assist further decisions in agriculture.