Over the years, similar to global markets, the Turkish electricity market has undergone significant reforms in order to meet the need for a more liberal market. One of these changes has been the establishment of a day-ahead market which is an organized market operated by the Market Operator, facilitates electricity trading and balancing activities one day prior to the delivery of electricity. In this market, the price that emerges in the hourly segments where the supply and demand curves intersect is called the Market Clearing Price. The growing need for optimization in areas such as more effective bid strategies, risk management, and increasing competition necessitated more accurate predictions of the MCP. The purpose of this study is to suggest market clearing price prediction models which are sensitive to price fluctuations using machine learning techniques. The dataset, which contains Market Clearing Price data, covers the period from January 2020 to December 2024. Furthermore, using feature engineering, external factors like lagged and non-lagged features, temporal indicators (weekday, weekend, and holiday), and renewable energy output will be included. The performance of the models is evaluated using metrics like R \(^{2}\) , Mean Absolute Error, and Root Mean Squared Error.