Forecasting the price of electricity has grown into an increasingly crucial part of the day-today operations of power providers. The efficacy of energy market participants, including producers as well as purchasers, may be increased by using accurate forecasting models. The process of investment planning also involves the consideration of price in a significant way. Using the well-known ARIMA approach, which is used for analysing and predicting time series data. The model is implemented using time series that are comprised of the day-ahead cost of electricity obtained from the EPEX energy exchanges. Forecasting the price of electrical power is an important responsibility in the energy business because it enables market players to make educated choices with regard to the trading of energy, the management of risks, and the distribution of resources. In the last few years, machine learning and additional statistical approaches have seen widespread use in an effort to enhance the accuracy of power price forecasts. This article presents and examines the effectiveness of a variety of machine learning and mathematical approaches. Additionally, the study discusses the implications of these findings. We give advice for picking the technique that is most appropriate for certain forecasting jobs after analysing the benefits and drawbacks of a variety of methodologies and comparing them. According to the findings, classical statistical models are outperformed in terms of accuracy by models based on machine learning that include artificial neural networks, support vector models, and random forests. The amount of volatility, seasonality, and load patterns are all factors that should be considered when choosing a model; nevertheless, the decision ultimately rests with the unique features of the electrical market. In conclusion, we analyse the shortcomings of the currently available methods and provide some suggestions for new lines of inquiry that may be pursued in the future to improve the precision and dependability of forecasts of future power prices