<p>Melanoma skin cancer poses a significant global health challenge due to its rapid progression and the high cost of treatment. Early and accurate detection is essential for improving survival rates and alleviating the burden on healthcare systems. This study proposes a deep learning ensemble model to address core challenges in skin cancer diagnosis, including precision and misclassification. The ensemble model combines the strengths of InceptionResNetV2, VGG16, and EfficientNetB4, effectively integrating their outputs for enhanced decision-making. The ISIC dataset, comprising dermoscopic images, is utilized for training, validation, and testing. Before training, the dataset undergoes preprocessing, cleaning, balancing, visualization, and evaluation phases to ensure high-quality input. The proposed model excels in detecting cancer from dermoscopic images, leveraging collective decision-making to outperform individual models in metrics such as precision, accuracy, sensitivity, F-score, and specificity. Notably, the deep ensemble model achieves remarkable results on ISIC 2020 dataset, demonstrating a 2.5% improvement in accuracy over existing approaches, with a precision of 98.40%, recall of 98.37%, and F1-score of 98.8%.</p>