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Agriculture Plays A Vital Role In Ensuring Food Security And Economic Stability, Yet It Faces Significant Challenges Due To Water Scarcity, Unpredictable Weather Conditions, And Crop Diseases. Conventional Irrigation Methods Often Result In Water Wastage, While Late Detection Of Plant Diseases Can Lead To Reduced Yield And Economic Loss. To Address These Challenges, This Study Proposes A Smart Irrigation System Integrated With Plant Disease Identification And Weather-based Automation. The System Utilizes Advanced Sensors And Internet Of Things (IoT) Technologies To Monitor Soil Moisture, Temperature, Humidity, And Weather Conditions In Real Time. Simultaneously, Image Processing And Machine Learning Techniques Are Employed To Detect And Predict Plant Diseases At An Early Stage. Based On Environmental Data And Disease Risk Assessment, The Irrigation System Is Automatically Controlled To Provide Optimal Water Supply, Ensuring Efficient Resource Utilization And Improved Crop Health. The Integration Of Disease Prediction With Weather-aware Smart Irrigation Not Only Conserves Water But Also Enhances Productivity And Sustainability In Agriculture. This Approach Highlights The Potential Of Combining IoT, Artificial Intelligence, And Predictive Analytics To Support Precision Farming And Tackle The Pressing Issues Of Climate Variability And Crop Diseases.