The accurate and timely diagnosis of skin diseases is crucial for effective patient treatment, yet existing diagnostic methods are often limited by their subjective nature, time consuming processes, and a global shortage of dermatologists. While Deep Learning (DL) models, particularly Convolutional Neural Networks (CNNs), have emerged as a promising solution for automated skin disease classification, their performance can be impeded by challenges such as limited dataset size, severe class imbalance, and the risk of overfitting. Moreover, the visual similarity of lesions to non-pathological structures presents a significant challenge to accurate distinction. This research addresses these limitations by proposing an end-to-end framework for the diagnosis and classification of seven common skin diseases. The proposed method integrates the state-of-the-art ConvNeXt-Tiny network with a comprehensive two-phase hybrid data augmentation pipeline. This pipeline, which includes both offline transformations and online probabilistic augmentations like Mixup and CutMix, is precisely designed to regularize the model and enhance its ability to generalize from a diverse range of visual data. After thorough data preprocessing, which included resizing and normalization, the ConvNeXt-Tiny model was trained using an AdamW optimizer, a one-cycle learning rate scheduler, and a smoothed Categorical Cross-Entropy loss function. The method's efficacy was rigorously evaluated using a broad suite of performance metrics. The experimental results indicate that the proposed approach obtained enhanced performance, with a classification of 95.21% accuracy, 93.89% precision, 89.87% recall, 98.62% specificity, 91.60% F1-score, and 98.98% AUC. These findings highlight the model's exceptional strength in discriminating between a wide variety of skin disorders and its ability to generalize effectively across diverse conditions. Furthermore, a detailed evaluation of base, quantized, and pruned models demonstrated that dynamic quantization reduced the model size by 93.1% (from 106.15 MB to 7.28 MB) and decreased the number of parameters from 27,825,511 to 1,908,480 with only a minor accuracy drop of 0.004, while magnitude pruning with 30% sparsity accelerated GPU inference by 1.53× (reducing batch processing time from 642.27 ms to 420.90 ms) with a slight accuracy improvement of 0.002, establishing a practical balance between high-performance classification and resource-efficient deployment. By providing a scalable, consistent, and objective solution for automated skin disease classification, this research contributes a powerful tool for Computer-Aided Diagnosis (CAD) systems, which can assist clinicians and ultimately help mitigate the diagnostic challenges in dermatology.