The identification and classification of melanoma are critical due to its status as the deadliest form of skin cancer, with a high propensity to metastasize if not detected early. Early diagnosis significantly improves treatment outcomes and survival rates. In the present investigation, we introduce a novel explainable hybrid deep learning architecture that amalgamates EfficientNetB0 as a pretrained feature extractor with a bidirectional long short-term memory (BiLSTM) network. The proposed model exhibits remarkable classification efficacy, attaining an area under the curve (AUC) of 1.0, an accuracy rate of 94.2%, a sensitivity measure of 91.9%, and a specificity level of 95.3%. Such findings underscore the model’s robust discriminative capability and its capacity for generalization across various categories of skin lesions. To address the clinical demand for transparency in AI systems, we integrate explainable AI (XAI) methods such as gradient-weighted class activation mapping (Grad-CAM) to visualize the critical regions influencing the model’s predictions. These graphical explanations help to substantiate the decision-making framework of the model, thereby enhancing the confidence of dermatologists and medical professionals.