In critical field medical diagnostics, early and accurate detection of skin cancer remains paramount for effective treatment, yet significant challenges persist to the subtle visual differences between benign and malignant lesions and limited access to specialist care. Addressing these challenges, skin cancer prediction study presents an innovative deep learning framework leveraging optimized YOLO architectures to revolutionize skin cancer classification. Utilizing a robust dataset of 33,126 dermoscopic images - with all malignant cases confirmed by histopathology and benign cases validated through expert consensus or longitudinal follow-up and developed and compared lightweight versions of YOLOv5 YOLOv8, Hybrid YOLO specifically engineered for clinical deployment. The methodology incorporated advanced optimizations including model pruning and TensorRT quantization to ensure efficient performance on edge devices like the NVIDIA Jetson Nano. This triad of models creates a comprehensive diagnostic ecosystem: YOLOv5’s precision reduces unnecessary biopsies, YOLOv8’s speed enables accessible population screening, and Hybrid YOLO’s enhanced sensitivity provides crucial safety-net detection for high-risk patients. Implemented together, they address the full spectrum of clinical needs - from rural health posts lacking specialists to advanced dermatology centers. The Hybrid YOLO’s particular strength in detecting subtle malignancies (validated on histopathology-confirmed cases) could transform monitoring of high-risk patients and lesions with ambiguous visual features. By combining these architectures with rigorous clinical validation, bridge a critical gap between AI innovation and real-world patient care. The Hybrid YOLO’s 98.57% accuracy and superior recall demonstrate that thoughtfully combined approaches can outperform standalone models where it matters most - catching more cancers earlier while maintaining diagnostic reliability.