Deep-sea debris poses a significant threat to marine life and human health. Traditional methods for underwater debris detection and classification are labour-intensive and inefficient. The major challenge for using vision robots or autonomous underwater vehicles(AUVs) to remove deep sea debris is to exactly identify the marine debris. Marine debris gets deformed, eroded, and blocked due to seawater. Marine debris changes its shape, size, and texture in sea environment. Sea environment is challenging for the task of debris detection because of weak light. Uncertainty about the task of debris detection is due to marine life, rocks, marine flora, fauna, algae, etc. This study aims to develop a robust deep learning model for underwater debris detection and classification using YOLOV8. We evaluate the performance of YOLOV8 against YOLOV3 and YOLOV5 on the JAMSTEC TrashCan dataset. By employing an anchor-free detection head, YOLOV8 demonstrates improved accuracy in detecting underwater debris of varying shapes, sizes, and textures. Here, we show that YOLOV8 achieves a mean Average Precision (mAP) of 0.5095, outperforming YOLOV3 (mAP: 0.31879) and YOLOV5 (mAP: 0.43608). Our findings underscore the potential of anchor-free YOLOV8 in addressing the challenges of underwater debris detection, which is crucial for marine conservation efforts.