In the context of electricity market transactions， price forecasting has increasingly become an indispensable component of decision-making mechanisms for energy enterprises and serves as a crucial basis for market participants to formulate bidding strategies. Accurate electricity price forecasts assist various trading entities in the power market in reducing bidding risks and maximizing their interests. Therefore， researching electricity price forecasting holds significant importance. However， due to multiple influencing factors such as meteorological conditions， load demand， line congestion， and policy changes， electricity prices exhibit complex uncertainties and notable volatility. To address this issue， methods for predicting electricity prices have diversified over time. Nevertheless， challenges remain in achieving precise forecasts due to the scarcity of high-quality trading data and inherent flaws in prediction algorithms. This paper reviews relevant research findings on electricity price forecasting both domestically and internationally. Firstly， it analyzes the mechanisms behind price formation along with its influencing factors while summarizing related theoretical research methodologies. Secondly， it provides a detailed overview of recent advancements in electricity price forecasting methods by categorizing them into four main areas： time series prediction models， traditional machine learning models， deep learning models， and hybrid models； each method is discussed thoroughly with critical analysis. Finally， from perspectives including influencing factors， data preprocessing techniques， method selection criteria as well as evaluation metrics， this study anticipates future trends in electricity price forecasting.