Skin cancer is one of the worst diseases in the world and requires early identification and treatment. Deep learning has recently become the primary approach for categorizing skin cancer, driven by its potential to enhance diagnostic accuracy significantly. In this study, our goal is to improve existing classification methods by using an ensemble fusion technique, merging the strengths of multiple models to achieve better performance. The importance of this research is emphasized by the urgent need for accurate and dependable skin cancer detection systems that can enable prompt treatment and decrease mortality rates. We used the ISIC dataset, a comprehensive compilation of dermoscopic images, to train and assess our models. The dataset’s diversity and size make it perfect for developing and testing advanced classification algorithms. Our investigation centres on three fundamental DL models: EfficientNetB3, VGG16, and ResNet50. Due to their varied architectural strengths and proven efficacy in image classification tasks, these models were chosen. Along with the performance of each model, we applied an ensemble fusion strategy (T3 fusion) to combine the results of the three models to enhance overall classification accuracy. The ensemble approach significantly outperformed each base model. The Weighted Ensemble model achieved precision, recall, and F1 scores of 97%, 96%, and 97%. The generalized performance found by calculating the training (98.3%) and validation (96.5%) indicates that our model is well-fitted, which can be crucial in clinical settings.