This paper provides a comprehensive review of time series forecasting techniques, covering traditional statistical methods, machine learning approaches, deep learning models, and hybrid methods. Time series forecasting is crucial in various fields, including finance, economics, weather prediction, and energy consumption. The paper discusses the evolution of forecasting techniques, from classical methods like ARIMA and exponential smoothing to modern approaches such as LSTM and RNN networks. It highlights the challenges in time series forecasting, including seasonality, trends, missing data, and non-stationarity. The paper also explores evaluation metrics like MAE, RMSE, and MAPE to assess forecasting accuracy. Future directions include the integration of explainable AI, probabilistic forecasting, and handling big time series data. The study emphasizes the need for robust and adaptive techniques to improve forecasting accuracy and reliability in dynamic environments. The paper concludes that ongoing research and innovation are essential for advancing time series forecasting in various domains.This paper provides a comprehensive review of time series forecasting techniques, covering traditional statistical methods, machine learning approaches, deep learning models, and hybrid methods. Time series forecasting is crucial in various fields, including finance, economics, weather prediction, and energy consumption. The paper discusses the evolution of forecasting techniques, from classical methods like ARIMA and exponential smoothing to modern approaches such as LSTM and RNN networks. It highlights the challenges in time series forecasting, including seasonality, trends, missing data, and non-stationarity. The paper also explores evaluation metrics like MAE, RMSE, and MAPE to assess forecasting accuracy. Future directions include the integration of explainable AI, probabilistic forecasting, and handling big time series data. The study emphasizes the need for robust and adaptive techniques to improve forecasting accuracy and reliability in dynamic environments. The paper concludes that ongoing research and innovation are essential for advancing time series forecasting in various domains.