Deep learning and machine vision technologies nowadays is a power artificial intelligence-based on industrial welding defect detection operations in manufacturing and require superior automated inspection systems. YOLOv8 represents the advanced stage of YOLO deep neural network architecture which brings powerful object detection features to fusion welding applications resulting in high accuracy for computer vision quality control technology. The integration between artificial intelligence and traditional inspection approaches now provides a viable route for reducing dependency on humans through automated methods that inspect conventional welds. The research executes YOLOv8 as a leading-edge deep learning structure which detects welding flaws automatically through machine vision systems for potential remote welding surface assessments. The proposed system stands apart from previously described systems because it combines high-resolution machine vision cameras with the YOLOv8 sophisticated convolutional neural network structure. Standardised remote visual inspection configurations were implemented to gather datasets from steel weld inspections while testing the system for various typical carbon steel welding defect shapes. The training portion of the deep learning model underwent evaluations in detail to assess both its real time deployment suitability and its defect identification and categorisation abilities. The testing process verified remarkable system performance with high accuracy reaching 98% confidence for its complex deep learning algorithms. The system offers better assessment speed than traditional inspection methods and simultaneously lowers the need for human involvement and offers total digital documentation for quality control purposes. The results demonstrate YOLOv8 to be a potential leading technology for the next-generation of industrial welding quality control systems as it persists robust surface defect identification across multiple inspection conditions.