This project employs an AI/ML model to predict the 52-week price performance of a stock using financial data extracted from the S&P 500 list of companies. The data is examined, visualized, and processed, followed by the implementation of support vector regression (SVR). The study presents the results alongside a conclusion and suggestions for future research. While SVR has been utilized for short-term stock price prediction (e.g., intraday and daily), its application to long-term forecasting has been limited. This study specifically applies SVR with a radial basis function (RBF) kernel to achieve accurate long-term predictions of stock price performance. Financial experts typically use fundamental or technical analysis to forecast stock performance, but such expertise often involves tacit knowledge that is challenging to transmit. This project aims to address this challenge by training a machine learning model to forecast a stock's one-year price performance with 70%+ predictive accurateness.