This paper introduces a novel framework for stock market prediction, designed to address the complexities of time series data in financial markets. By leveraging residual modeling techniques, the model enhances forecasting accuracy while tackling challenges such as noise and non-stationarity. Our study focuses on three major Indian financial indexes: SENSEX, NIFTY50, and NIFTY CONSUMPTION, using a dataset of 2270 trading days with 450 days dedicated to testing. The proposed model integrates clustering techniques and Support Vector Regression (SVR) to analyze immediate price movements while also identifying overarching market patterns. After an initial prediction phase, residual errors are computed and further modeled, allowing the system to correct any remaining inaccuracies. This residual-based approach ensures more precise forecasts, outperforming traditional models in terms of error reduction. Our results demonstrate that the model consistently surpasses the performance of reservoir computing (RC), which has been known for its strong predictive capabilities compared to deep learning models. By achieving lower mean squared errors (MSE) and superior predictive accuracy across different market conditions, the proposed novel framework presents a scalable and robust solution for time series forecasting. This makes it a valuable tool for investors and financial analysts navigating volatile stock markets.