In this study, we propose novel classification models that integrate the Quasi-Reversibility Method (QRM) with advanced machine learning techniques to enhance the prediction of option prices. The QRM, well known for solving the Black-Scholes equation under challenging conditions, forecasts option prices one day in advance. By leveraging the QRM-derived minimizer with machine learning classification models, we aim to categorize options as either increasing or decreasing in value. This integration enables more refined categorization of financial instruments, blending numerical analysis and predictive modeling. Using these classifications, we implement trading strategies to enhance the predictive power of QRM extrapolations. These strategies leverage both historical data trends and QRM forecasts, potentially improving trading efficiency, especially in volatile markets. To validate our approach, we collected 23,548 real-world options data points across various time periods and market conditions, creating a comprehensive dataset to rigorously test our models. This data, combined with the QRM minimizer, trains machine learning models, including decision trees, random forests, gradient boosting classifiers, k-nearest neighbors, and neural networks. The models’ performance is assessed using metrics such as accuracy, precision, and recall to evaluate the framework’s consistency and reliability. Our goal is to identify which models, paired with QRM-derived features, perform most effectively for option price prediction. This study ultimately aims to provide a robust predictive tool that is practical for short-term trading and grounded in established mathematical finance methods.