<p>Support Vector Machines are widely used for classification tasks due to their robustness in handling complex data distributions. However, traditional kernel functions often struggle with nonlinear boundaries in real-world datasets. This study pioneers an approach that enhances Support Vector Machine performance by integrating diverse sigmoid-based functions, including the Richards, Weibull, Michaelis-Menten, Double Boltzmann, and Swish models. These functions improve data separability by offering adaptive decision boundaries that align more naturally with clustered or overlapping classes. The proposed Sigmoid-Optimized Support Vector Machine is evaluated on various datasets, demonstrating up to 91.5% improvement in classification accuracy, reduced misclassification rates, and enhanced generalization capabilities compared to conventional Support Vector Machine kernels. Experimental results indicate that sigmoid-based fits improve feature scaling, optimize hyperplane placement, and enhance adaptability in high-dimensional spaces. This novel framework efficiently manages computational complexity, enabling more accurate and robust data categorization, which is especially beneficial for applications ranging from biomedical analysis to energy forecasting.</p> Graphical abstract <p></p>