This paper evaluates the performance of machine learning (ML) methods against traditional statistical methods in time series forecasting using a subset of 1045 monthly time series from the M3 Competition. The study compares the accuracy and computational requirements of eight popular ML methods (including MLP, BNN, RBF, GRNN, KNN, CART, SVR, and GP) with eight traditional statistical methods (including SES, Holt, Damped, Comb, Theta, ARIMA, and ETS). The results show that statistical methods generally outperform ML methods in terms of accuracy across all forecasting horizons. ML methods, while more accurate in some cases, require significantly more computational resources and are less efficient. The study also highlights the importance of proper preprocessing to improve forecasting accuracy, as well as the need for objective and unbiased testing of forecasting methods through large-scale competitions. The findings suggest that while ML methods have potential, they are not yet superior to traditional statistical methods in forecasting accuracy. The paper concludes that further research is needed to improve the performance of ML methods and to better understand their limitations.This paper evaluates the performance of machine learning (ML) methods against traditional statistical methods in time series forecasting using a subset of 1045 monthly time series from the M3 Competition. The study compares the accuracy and computational requirements of eight popular ML methods (including MLP, BNN, RBF, GRNN, KNN, CART, SVR, and GP) with eight traditional statistical methods (including SES, Holt, Damped, Comb, Theta, ARIMA, and ETS). The results show that statistical methods generally outperform ML methods in terms of accuracy across all forecasting horizons. ML methods, while more accurate in some cases, require significantly more computational resources and are less efficient. The study also highlights the importance of proper preprocessing to improve forecasting accuracy, as well as the need for objective and unbiased testing of forecasting methods through large-scale competitions. The findings suggest that while ML methods have potential, they are not yet superior to traditional statistical methods in forecasting accuracy. The paper concludes that further research is needed to improve the performance of ML methods and to better understand their limitations.