This paper delves into the intricate world of financial markets, where accurate prediction of stock prices plays a pivotal role in guiding investment strategies and financial decision-making. In an era dominated by vast amounts of data, the application of advanced time series analysis models has become increasingly critical for extracting meaningful insights from market trends. This paper explores the efficacy of various deep learning models, including Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), and an enhanced version of GRU (Improved GRU), in predicting stock prices. Through rigorous evaluation, the paper demonstrates that the Improved GRU model outshines its counterparts in forecasting accuracy, as evidenced by its superior performance across multiple evaluation metrics such as Mean Squared Error, Mean Absolute Error, Root Mean Squared Error, and R-squared Score. The implications of these findings are vast, suggesting that the Improved GRU model can serve as a powerful tool for investors and financial analysts seeking to navigate the complexities of the stock market with greater precision. Furthermore, the study opens up new avenues for future research, emphasizing the need for further enhancements in prediction models through the integration of more granular data sources, exploration of hybrid models, and the application of machine learning techniques to understand the multifaceted dynamics of financial markets better. This paper not only advances our understanding of stock price prediction models but also sets the stage for the development of more sophisticated and accurate forecasting tools in the field of financial time series analysis.