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The safety and security of heavy-duty vehicles have become a significant concern due to their frequent operation in high-risk industrial and logistical environments. Traditional safety mechanisms such as mirrors and alarms are no longer sufficient in preventing accidents caused by blind spots, limited maneuverability, and human error. This project addresses these limitations by proposing an AI-based real-time monitoring and alert system designed specifically for heavy-duty vehicles. The system utilizes advanced computer vision and deep learning techniques to detect surrounding objects, monitor the vehicle’s environment, and provide timely audio-visual alerts to the driver, enhancing overall situational awareness.At the core of this system lies the use of pre-trained object detection models, such as YOLOv5 and YOLOv8, which are capable of identifying pedestrians, vehicles, and obstacles with high accuracy in real-time. These models are integrated with a user-friendly Streamlit interface and OpenCV processing pipeline to display live video feeds and detection results. Additionally, audio alerts are generated using Pygame and Google Text-to-Speech (gTTS) to ensure the driver is promptly notified of potential hazards. An analytics dashboard is also incorporated using Plotly to record and visualize detection history, speed trends, and safety events, enabling both on-the-spot decisions and long-term operational improvements. The proposed system demonstrates a scalable and practical solution to enhancing the safety protocols of large vehicles. By merging AI technologies with intuitive interfaces and real-time analytics, the system can significantly reduce accident rates and operational risks.