Agriculture is one of the most fundamental resources of food production and also plays a vital role in keeping the economy running of every nation by contributing to the Gross Domestic Production. But there are several issues related to traditional methods of agriculture such as excessive wastage of water during irrigation of field, dependency on non- renewable power source, time, money, human resource, etc. Since every activity nowadays becoming smart it needs to smartly develop agriculture sector for the growth of the country. The Smart Irrigation System using IoT and Machine Learning projects present a system through which the water utilization is monitored according to the soil moisture and environmental conditions. A machine learning model is used to estimate the growth of the shoot length using the above-gathered parameters and assist the farmer. Various sensors like Soil moisture sensors are used to record the soil moisture levels and soil pH levels, the humidity, and temperature is measured using DHT11 a basic, ultra low-cost digital temperature, and humidity sensor. This data is analyzed to generate two outputs. The first is to decide the amount of water required henceforth using current and past data to automatically triggering the irrigation system. The second is to capture the image of the plant by using stationary camera and after clicking the picture if there is any problem or defect, it will immediately notify the farmer to take some action. This will reduce the efforts of the farmer and help to improve quality farming.