Rice isn't just essential to global food security; disease outbreaks and poor soil health management stymie its growth. Often, traditional rice farmers lack the knowledge or resources to diagnose plant diseases and monitor soil conditions in real time. Existing solutions typically focus on either soil health monitoring or disease detection, but not both, leaving farmers unable to respond to the identified threat. This project will mitigate this by combining real-time monitoring, disease classification, and a recommendation system into a single solution. Poor disease detection and inadequate health examinations of soils commonly result in decreased rice productivity. The proposed research is also focused on the development of an intelligent system equipped with IoT sensors to monitor soil parameters such as moisture, nitrogen concentration (NPK), and temperature in real time, and a machine-learning-based system capable of classifying 15 different rice diseases. The system also includes a recommendation engine that provides actionable recommendations for treating an illness, making it a complete soil and crop health management tool. The system is based on a transfer learning model (MobileNetV2) that classifies rice illnesses using image classification. The model was trained on 22,688 images of rice diseases, achieving a detection accuracy of 96.42%. The system was also highly accurate for monitoring soil health, with minimum standard deviations of 0.20% and 0.22 for soil moisture and nitrogen levels, respectively. The results obtained reflect the effectiveness of the developed system in enhancing the farming process by enabling farmers to identify diseases at early stages and improve soil conditions. Lastly, the methodology enhances rice production, reduces crop losses, and helps achieve global food security.