This paper presents a novel approach to smart agriculture that integrates the Internet of Things (IoT) and Machine Learning (ML) to enhance crop prediction and management. The system aims to provide farmers with real-time, accurate data and recommendations to optimize crop yields and quality. Key features include: 1. **IoT Integration**: Real-time monitoring of environmental conditions such as temperature, moisture, and rainfall using IoT devices like the DHT22 sensor and Arduino Uno. 2. **Machine Learning Algorithms**: Utilizes Decision Tree, K-NN, and Support Vector Machine (SVM) algorithms to predict the most suitable crops based on soil characteristics, climate data, and historical crop performance. 3. **Data Collection and Analysis**: Collects live data from IoT devices and historical data from government websites and APIs, processing it to provide detailed insights. 4. **User Interface**: A responsive, multilingual website allows farmers to input field details and receive crop recommendations, along with fertilizer suggestions. 5. **Performance**: The system demonstrates high accuracy in crop prediction, with Decision Tree showing the highest precision among the tested algorithms. The proposed system addresses the challenges faced by Indian farmers, such as climate change, low yields, and resource management, by providing data-driven solutions. It contributes to sustainable agricultural practices, enhances crop quality, and supports food security.This paper presents a novel approach to smart agriculture that integrates the Internet of Things (IoT) and Machine Learning (ML) to enhance crop prediction and management. The system aims to provide farmers with real-time, accurate data and recommendations to optimize crop yields and quality. Key features include: 1. **IoT Integration**: Real-time monitoring of environmental conditions such as temperature, moisture, and rainfall using IoT devices like the DHT22 sensor and Arduino Uno. 2. **Machine Learning Algorithms**: Utilizes Decision Tree, K-NN, and Support Vector Machine (SVM) algorithms to predict the most suitable crops based on soil characteristics, climate data, and historical crop performance. 3. **Data Collection and Analysis**: Collects live data from IoT devices and historical data from government websites and APIs, processing it to provide detailed insights. 4. **User Interface**: A responsive, multilingual website allows farmers to input field details and receive crop recommendations, along with fertilizer suggestions. 5. **Performance**: The system demonstrates high accuracy in crop prediction, with Decision Tree showing the highest precision among the tested algorithms. The proposed system addresses the challenges faced by Indian farmers, such as climate change, low yields, and resource management, by providing data-driven solutions. It contributes to sustainable agricultural practices, enhances crop quality, and supports food security.