Skin cancer is one of the most common and preventable cancers, yet early detection remains challenging, especially in resource-limited settings with scarce specialized healthcare. This study develops an AI-driven diagnostic tool optimized for embedded systems to address this gap. Using transfer learning with MobileNetV2, the model was trained for multi-class classification of skin lesions and optimized with TensorRT for deployment on the NVIDIA Jetson Orin Nano. Evaluations focused on model size, inference speed, throughput, and power consumption, balancing performance with efficiency. The optimized model maintained an F1-score of 65% while significantly reducing model size and energy consumption. Despite not achieving state-of-the-art accuracy, this research prioritizes real-world feasibility, demonstrating AI’s potential for accessible diagnostics in low-resource environments. The methods presented extend beyond skin cancer detection, with applications in other medical and autonomous systems, highlighting the broader impact of AI-driven solutions on global healthcare accessibility.