Melanoma, a dangerous form of skin cancer, is rapidly spreading and poses a serious threat to global health. Early and accurate detection is essential for saving lives, yet conventional diagnostic approaches often fall short in precision. This research presents a robust detection system utilizing ResNet34, a deep learning model known for its efficiency in image classification tasks. The study emphasizes effective preprocessing and data augmentation to improve image quality and diversity. ResNet34 was employed for feature extraction and classification, achieving an impressive accuracy of 98%, with a precision of 99% for malignant and 97% for benign cases. Recall scores reached 97% and 99% for malignant and benign cases respectively, resulting in a balanced F1-score of 0.98. ROC analysis further confirmed the model’s reliability in distinguishing melanoma from benign lesions. The classification report supports its effectiveness and potential for real-world deployment. This work highlights the powerful role of deep learning in enhancing medical diagnostics and offers a practical solution for early melanoma detection, contributing to global skin cancer prevention efforts.