This research presents an agritech system that integrates IoT and machine learning to revolutionize agriculture. IoT sensors monitor critical factors such as soil moisture, temperature, and crop health, collecting data for analysis through cloud computing and ML algorithms. The system provides crop recommendations based on soil composition, climate, and crop performance, enhancing crop yield, optimizing resource use, and promoting sustainability. Key methodologies include IoT sensor deployment, data preprocessing, and ML model evaluation. Wireless sensors capture environmental parameters, transmitting data via IoT-enabled devices to cloud platforms for analysis. ML algorithms process historical and real-time datasets to recommend optimal crops, ensuring enhanced productivity and resource efficiency. Experimental results indicate that the Random Forest algorithm achieved 99% accuracy, while Naive Bayes recorded 98% accuracy in crop prediction. Comparative analysis shows that the system enhances resource efficiency by 25% and improves crop yield by 20% over traditional methods. Challenges like IoT accessibility for small-scale farmers and data privacy are acknowledged, encouraging further research.