Agriculture is the backbone of India, and efficient water management is central to sustainable crop production. Most farmers in Thiruvallur, Tamil Nadu, resort to traditional irrigation practices that lead to either over-irrigation or inadequate irrigation. The adverse effects are yield loss and a water scarcity threat. This paper proposes the development of an intelligent AI-controlled smart irrigation system harnessing IoT-based real-time monitoring and deep learning techniques to optimize water use. A sensor network comprising soil moisture, temperature, humidity, rainfall, and water level sensors will be used to harvest environmental data. This data can be input to a Long Short-Term Memory CNN model that will provide the astute irrigation estimation sense. To supplement the robustness in prediction, synthetic data generation can be incorporated into training the model using Gaussian distribution and Generative Adversarial Networks (GANs). The innovation in this system is an AI-based alert system capable of informing local farmers in Tamil through beeping alerts and voice notifications regarding irrigation scheduling. Scheduling of irrigation will also be adaptive and dynamic reinforcement learning-based schedule-changing irrigation plans by processing historical and real-time data. The purpose of this system is to connect AI, IoT, and real-time environmental adaptability to create a scalable and inexpensive solution for the optimal use of water resources in smart agriculture.