The exchange-traded funds have seen greater condition as an entertainment choice in modern financial markets as they were designed for providing diversified exposure in equities as well as bonds and commodities types of asset classes. Despite the steadily increasing attractiveness and usage of such products, there still exists a gaping research gap in predicting their performance relative to sectors, especially in a comparatively emerging market such as India. This work intends to fill that gap by comparing Machine Learning models such as Random Forest and SVM with traditional models for sector-wise performance forecasting like ARIMA and Holt-Winters. Based on data available in investing.com , the work analyzes daily ETF prices across seven key sectors—Pharmaceuticals, FMCG, Banking, IT, Infrastructure, Consumption, and Healthcare—from 2021 to 2024. Performance of the model is evaluated by overall fit criterion: R2 (coefficient of determination), Mean Absolute Error (MAE), Difference in Square Errors (DSE). Machine learning techniques have been found to considerably outperform classical statical models in capturing complicated market activities, especially in volatile sectors like Infrastructure and Banking. Hill-Winters and ARIMA models reliably forecast stable sectors, such as Pharmaceuticals and Healthcare, while their kings fade away in overly dynamic markets. These research observations offer information to assist investors, portfolio managers, and policymakers as an illustration of the possibilities that exist for machine learning applications in financial forecasting. The integration of machine learning approaches should thus be magnified to improve on ETF price forecasting and investment strategies.