In today's dynamic industrial landscape, the need for advanced systems capable of swiftly detecting and responding to on-target or off-target payload object drops in real-time has become increasingly critical. This research paper intro-duces a groundbreaking initiative aimed at revolutionizing safety protocols, optimizing cargo handling procedures, and enhancing operational efficiency through the development of an innovative solution: the "Detection of On-Target Payload Object Drop and Automatic Alert Generator." Acknowledging the pressing demand for accurate, automated, and immediate responses to on-target payload object drops across various sectors including logistics, manufacturing, and security, this research addresses the inherent challenges posed by the lack of a reliable and real-time detection system. Operational disruptions, cargo damage, worker safety concerns, and inefficiencies resulting from accidental drops of payload objects have underscored the urgent need for proactive measures. The proposed system leverages cutting-edge technologies such as the YOLO-NAS deep learning model and the NVidia DGX A100 GPU server to deliver unparalleled performance and precision. By harnessing the power of artificial intelligence and high-performance computing, this intelligent system is designed to accurately detect payload object drops and generate automatic alerts in real-time, tailored to the specific requirements of clients or applications. By integrating seamlessly into existing workflows, this innovative solution promises to not only mitigate the risks associated with payload object drops but also streamline operations, minimize downtime, and uphold the highest standards of safety and efficiency. Through a comprehensive analysis of industry-specific challenges and practical implementation strategies, this research paper offers valuable insights into the transformative potential of advanced detection systems in safeguarding valuable cargo, protecting personnel, and optimizing operational performance.