Early detection of skin cancer is very crucial as it poses a huge health risk to those afflicted by it. The study presents an empirical analysis using various machine learning algorithms for skin cancer detection with a specific focus on hybrid model techniques. Leveraging advancements in machine learning and image analysis, among all the models, the best result was achieved by Random Forest and Gradient Boosting, which had an accuracy of 91% and AUC of 0.94 outperforming other hybrid classifiers. The analysis further emphasizes key methodologies including data acquisition, augmenting methods, feature extraction using color histograms and local binary patterns (LBP), and hyperparameter optimization. Detailed performance metrics highlight the model’s capabilities against the competing ones by distinguishing between benign and malignant lesions. The study fills gaps and aims to bridge the promising application of hybrid machine learning methodologies to enhance the diagnostic precision of skin lesions. Ultimately, this research underscores the potential of ML advancements to revolutionize skin cancer detection and improve diagnostic accuracy in dermatology.