With growing concerns about global water scarcity, agriculture faces a significant challenge in optimizing water usage. Traditional irrigation methods often lead to water waste due to imprecise scheduling and lack of real-time data on crop needs. This paper proposes an artificial intelligence (AI) and sensor-driven system for irrigation management, promoting water conservation and efficient irrigation practices. The system integrates real-time data from soil moisture sensors, temperature, and humidity sensors with a pre-trained machine learning model. By analyzing this data and considering crop selection, the model determines the optimal water delivery for different crops. The system utilizes Arduino, Node MCU (ESP8266) microcontrollers, the Blynk cloud platform, and an Internet of Things (IoT) application for data acquisition, processing, and user interaction. This approach offers real-time monitoring, automated irrigation control based on AI predictions, and user-friendly crop selection, fostering efficient water utilization and potentially increasing crop yields.