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Agriculture is undergoing a global transformation driven by automation, IoT, and data-centric decision-making. One of the most critical challenges facing modern agriculture is the inefficient use of water and fertilizers, which directly impacts crop yield and long-term sustainability. This paper presents a comprehensive IoT-driven irrigation and fertilization system that uses real-time soil, environmental, and nutrient data to optimize resource use. The proposed system integrates soil-moisture sensing, environmental monitoring, cloud-based analytics, and automated pump control. Weather data is additionally incorporated to prevent unnecessary irrigation, particularly during periods of expected rainfall. The system is designed using low-cost components such as an Arduino Nano controller, soil-moisture probe, DHT11 sensor, and NPK sensor. Experimental results indicate significant improvements in irrigation efficiency, nutrient balance, and reduction of human supervision, demonstrating the viability of the system for small and medium-scale farmers. This work contributes a scalable framework that can be extended using AI- driven prediction models, long-range communication, and renewable energy support