A skin lesion is a strange development of a body component that is located on the skin. Lesion identification at the earliest stages is essential. This is where the melanoma illness is detected. The occurrence of melanoma has increased dramatically in the modern period. Melanoma has the potential to be fatal. It might be utilized to obtain quantitative data regarding a body part lesion in the medical field. Investigating the digital photos of the epidermal lesion on the affected body part is a straightforward method. The extraction of features is a crucial component in this study work that might be utilized to appropriately examine and assess the image. Various photos underwent pre-processing, and characteristics were taken out of them. An additional crucial step in identifying an injury on the body part is image pre-processing. In the initial phases of melanoma diagnosis, we have suggested use the SVM approach to identify the disease. The methodology utilizes the capabilities of the Support Vector Machine Algorithm (SVM) and image processing techniques. The Support Vector Machine algorithm is in employment for the objective of categorization. As the input to the classification algorithm, the characteristics are provided. The support vector classification is the classifier that needs to be applied. Python is the method employed, and the confusion matrix is employed to generate the findings for improved precision. The paper compares several precision functions and provides an overview of the technique used.