Stock price prediction is a crucial, yet highly challenging task due to the nonlinear, volatile, and complex nature of financial markets. Traditional forecasting methods often struggle to capture intricate patterns in real-world data. This study proposes a machine learning-based approach using Gaussian Process Regression (GPR) to predict short-term stock prices, specifically targeting the Open, High, and Low values of stocks listed under the NIFTY 500 index. The model incorporates a rich set of financial indicators including previous close price, trading volume, 52-week highs/lows, and the percentage increase or decrease in value over the past 30 days and past 1 year. The dataset is cleaned, log-transformed to reduce skewness in highly varying financial attributes, scaled and evaluated using k-fold cross-validation to ensure robust performance estimation. The GPR model is trained with a composite kernel combining Radial Basis Function (RBF), WhiteKernel, and ConstantKernel. The model outputs not only point predictions but also provides uncertainty estimations, making it highly suitable for real-world financial decision-making. Evaluation metrics such as Mean Squared Error (MSE), R2 score, Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) are computed, and the predictions are visualized for interpretability. Experimental results demonstrate that the proposed model is both accurate and interpretable, with the ability to support investor decisions through data-driven insights. The system achieves a MAPE of 0.748567% and an R2 score of 0.997453, indicating promising performance in forecasting key stock indicators.