The development of an intelligent and adaptive crop monitoring and disease detection system is motivated by the challenges associated with paddy crop cultivation and the critical need for sustainable farming practices. Paddy farming faces numerous obstacles, including unpredictable weather, pest outbreaks, nutrient deficiencies, and disease infections, which contribute to reduced crop yields and financial losses for farmers. The lack of real-time data and efficient monitoring tools further exacerbates these issues. An automated IoT-based monitoring system, integrated with a network of sensors, can dynamically track environmental conditions to provide actionable insights. The proposed system uses sensors to monitor temperature, humidity, soil moisture, water levels, PH, and NPK nutrient levels, all connected via Wi-Fi modules (ESP8266) for seamless data transmission to the cloud and displays it on a mobile application, ensuring that farmers have real-time access to vital information. A key feature of the system is its ability to detect disease using a Convolutional Neural Network (CNN). The CNN model analyzes real-time images of paddy leaves to detect and classify potential diseases, enabling farmers to take preventive measures promptly. The system also generates automated alerts when sensor readings deviate from normal thresholds at each stage of paddy growth. These alerts are sent to farmers via a mobile application, ensuring timely interventions and efficient resource management. The primary goal of this system is to optimize crop management by enabling precise leaf disease detection and monitoring, thus reducing losses and enhancing yields. By providing real-time updates on crop growth stages and health conditions, the system empowers farmers with the tools needed to make informed decisions.