Seismic reservoir horizon picking is an active area of research, as it is one of the primary methods of interpretation. A problem with Machine Learning (ML) based methods is that they are prone to being trapped in local minima during optimization, leading to overfitting. This research mainly uses the Support Vector Machines (SVM) method to overcome overfitting and the complex mathematical operations of linear and nonlinear ML methods. We used eleven seismic attributes in our process; there is no need to define a point on the horizon to start hydrocarbon horizon tracking in a local window, which restricts the algorithm to window length and operator choice. Before feeding the input data to SVM, a Principal Component Analysis is performed as a dimensionality-reduction technique to improve efficiency and accuracy. Optimal horizon tracing requires tuning parameters, including regularization control C and the Gaussian kernel width (gamma). In most of the studied cases, the first parameter's effect was negligible for larger gamma values. A mediate-to-coarse Gaussian kernel is more useful for selecting the true horizon locations from multiple. While amplitude information is crucial for feature selection, it is also important to consider complementary information such as phase and frequency-related attributes. Our approach to learning and optimization showcases the robustness and reliability of our workflow for horizon picking. This is evident even in challenging areas with a low signal-to-noise ratio, similar to the outcomes achieved through deep learning in faulted zones, and it allows us to complete tasks in a relatively short amount of time.