Skin cancer is one of the most prevalent and life-threatening forms of cancer, with its incidence rapidly increasing over the past few decades. Early detection plays a crucial role in improving the survival rate of individuals diagnosed with skin cancer, particularly melanoma, which is the deadliest form. Traditionally, skin cancer diagnosis has relied on time-consuming and invasive methods such as skin biopsy. However, with advancements in technology, automated diagnosis through intelligent techniques has shown the potential to expedite the detection process and increase diagnostic accuracy. Among the various methods explored for skin cancer detection, Convolutional Neural Networks (CNNs) have emerged as one of the most effective deep learning models. CNNs are capable of learning and extracting intricate features from skin images, making them highly suitable for melanoma classification tasks. In this study, a deep CNN model is designed and evaluated for classifying melanoma from other skin lesions. Key parameters of the CNN, such as filter size and the number of filters, are optimized using the Particle Swarm Optimization (PSO) algorithm. This optimization process aims to minimize classification errors and enhance the overall performance of the model. The simulation results reveal that the proposed method outperforms existing frameworks, achieving a remarkable accuracy rate of 96% on the utilized dataset, demonstrating the effectiveness and reliability of the proposed approach for melanoma detection.