Stock performance models are tightly linked to recent data. The ability to accurately predict stock prices has many advantages. It helps investors better understand future market trends and price movements and make the right decisions. By analyzing short- and long-term market movements, it can guide fast and strategic decisions. Machine learning algorithms are among the methods successfully used in stock price forecasting. Within the scope of the study, Linear Regression, Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM) and Support Vector Regression (SVR) models were used to estimate future price movements by analysing historical data and the best performance was obtained in the LSTM model (R-Square = 0.997). However, the high R-Squared value indicates that the model overfits the data set, the model loses its generalisation ability and thus there is a danger of overfitting. Therefore, necessary precautions were taken against possible overfitting situations and the model was ensured to make reliable predictions with various methods.