This paper presents a deep learning-based approach for stock price prediction in financial markets. The problem of accurately predicting future stock price movements is of crucial importance to investors and traders, as it allows them to make informed investment decisions. Deep learning, a branch of artificial intelligence, offers new perspectives for meeting this complex challenge. Deep learning models, such as deep neural networks, are capable of extracting complex features and patterns from large amounts of historical data on stock prices, trading volumes, financial news and data. other relevant factors. Using this data, deep learning and machine learning models can learn to recognize trends, patterns, and non-linear relationships between variables that can influence stock prices. Once trained, these models can be used to predict future stock prices. This study aims to find the most suitable model to predict stock prices using statistical learning with deep learning and machine learning methods RNN, LSTM, GRU, SVM and Linear Regression using the data on Apple stock prices from Yahoo Finance from 2000 to 2024. The result showed that SVMmodeling is not suitable for predicting Apple stock prices. In comparison,GRUshowed the best performance in predicting Apple stock prices with a MAE of 1.64 and an RMSE of 2.14 which exceeded the results of LSTM, Linear regression and SVM. The limitation of this research was that the data type was only time series data. It is important to note, however, that stock price forecasting remains a complex challenge due to the volatile nature of financial markets and the influence of unpredictable factors. Although deep learning models can improve prediction accuracy, it is essential to understand that errors can still occur.