Skin cancer is widely acknowledged as a particularly dangerous type of cancer, and there has been a large increase in death rates due to a lack of awareness about its symptoms and preventive measures. Hence, it is crucial to identify cancer in its initial phase to minimize its advancement. The shortage of competent physicians, insufficient medical equipment, and arduous diagnostic procedures for identifying malignant skin lesions pose a severe obstacle to timely life-saving interventions. The utilization of image processing and deep learning models can effectively address these challenges by accurately identifying the malignancy of skin cancer lesions. Integrating an optimizer into the deep learning classifier model will enhance the extraction of pertinent features from visual images by dynamically modifying the model’s hyperparameters. An optimizer enhances the acquisition of features during the training stage of the deep learning model in order to achieve the global optimum more quickly. Moreover, this modification will enhance the training accuracy and improve other performance indicators, hence facilitating the evaluation of the model. This study compares the performances of different optimizers, such as Teaching-Learning-Based Optimizer (TLBO), Grey Wolf Optimizer (GWO), Dragonfly Algorithm (DA), and Wildebeest Herd Optimizer (WHO), when combined with various deep learning models like GoogLeNet and ResNet50. The objective is to enhance the exploration of parameter space and achieve global optima for a classification problem. The deep learning model and optimizer were applied to the PAD_UFES_20 and HAM10000 datasets, which are well-known datasets for skin cancer. The deep learning model, in conjunction with the optimizer, has been assessed using particular criteria like precision, recall, F1-score, and accuracy.