Tele-dermatology allows for the remote diagnosis of melanoma by utilizing cutting-edge technology to facilitate meetings and exchange images. Utilized 43,141 dermoscopic images from the two massive datasets, HAM10000 and ISIC2020. Lesion_id, image_name, age, sex, and area were significant attributes that were consistent across the two datasets. A ‘target’ trademark was included throughout the model construction process to provide trustworthy melanoma differentiating evidence. During the preprocessing phase, the Differentiation Restricted Versatile Histogram Evening out (CLAHE) technique was applied to obtain the best possible difference expansion and sound decrease. Morphological opening was introduced to the CLAHE-handled images to emphasize qualities even more. After a review of many image segmentation techniques, region-based division was shown to be the most effective due to its ease of use and high diagnostic yield for melanoma. Careful alignment was completed in order to work on model layout and make sure the goal variable circulation was achieved with greater efficiency. Work done on transfer learning combined with deep learning models, such as DenseNet, ResNet, Inception, Xception and EfficientNet. After moving forward, the suggested half-and-half engineering achieved impressive accuracy, demonstrating its genuine potential to advance melanoma diagnosis in tele-dermatology.