This research examines the coherence between regression models and forex trading concentrating on the currency pairs EUR/INR, GBP/INR, and USD/INR. Investigating the ability to dissect and forecast data using machine learning techniques exchange rates on a daily, weekly, and monthly basis using datasets collected over 11 years (January 01, 2012, to January 01, 2023) for the three distinct time frames. The research tested six supervised learning models including regression with a single independent variable (linear regression), regression using multiple independent variables (multiple regression), k-nearest neighbor, regression with decision trees, regression using random forests, and support vector regression. In certain scenarios, models provide significant accuracy for specific currency pairs and time frames.