Efficient irrigation management is a critical factor for enhancing agricultural productivity and conserving water resources, particularly in semi-arid regions such as the Lower Chenab Canal (LCC) region of Punjab, Pakistan. This study explores the integration of remote sensing technology and machine learning algorithms to develop a sophisticated irrigation management system aimed at optimizing water use and improving crop yields. This research involved collecting high-resolution satellite imagery, climatic data, and field observations to monitor crop health, soil moisture levels, and evapotranspiration rates across different cropping seasons. These data were utilized to train machine learning models capable of predicting crop water requirements with high accuracy. The core of our methodology lies in the application of various machine learning algorithms, including Random Forests, Support Vector Machines, and Neural Networks, to analyze the complex interactions between climatic variables, soil properties, and crop phenology. The predictive model developed was used to generate dynamic irrigation schedules tailored to the specific needs of different crops and growth stages. Field trials were conducted across multiple farms in the LCC region, comparing the performance of our technology-driven irrigation management system with the traditional irrigation practices. The results demonstrated a significant reduction in water usage, with up to 30% savings achieved without compromising crop yields. Additionally, the optimized irrigation schedules contributed to improved soil health and reduced incidences of waterlogging and salinity. This study highlights the potential of remote sensing and machine learning technologies to transform irrigation management, offering a scalable and cost-effective solution for farmers in water-scarce regions. By providing real-time insights and actionable recommendations, our approach empowers farmers to make informed decisions, promoting sustainable agricultural practices and ensuring long-term water resource sustainability.