In the context of global stock exchanges, accurately forecasting stock prices has become crucial for large companies, banks, and the global economy, where every advantage can mean the difference between success and failure. This study addresses the complexity of stock price prediction, which is influenced by numerous unpredictable factors such as social media information, financial reports, technical indicators, and the stock price itself. Due to the diverse nature and complexity of the data, effective analysis requires selecting appropriate methods and developing suitable models. This research examines four key analytical methods: fundamental analysis, technical analysis, sentiment analysis, and time series analysis. The study explores the potential of combining these methods into an ensemble to enhance forecasting accuracy. An ensemble allows for the integration of various information sources and adapts models according to current conditions, leading to a deeper understanding and more accurate stock price predictions. The aim of this work is to evaluate the effectiveness of different analytical models for stock price prediction and compare their performance with an ensemble that combines the results of multiple individual models. Specifically, the research will cover the performance analysis of each model, including fundamental analysis, technical analysis, sentiment analysis, and time series analysis. Each of these models provides different insights and uses specific data sets, making it crucial to understand their strengths and limitations in the context of stock price prediction. After evaluating the effectiveness of individual models, the study will focus on developing and implementing an ensemble model that integrates the results of all examined models. The ensemble model, which employs various approaches and methods, facilitates the integration of individual models and can provide a more comprehensive and precise stock price assessment. The goal is to determine whether combining different models can improve forecasting accuracy compared to using individual models and to assess the benefits of this integration in real-world market conditions. Through this comparison, the study aims to identify the most effective approaches and techniques for stock price analysis and provide recommendations for applying these methods in practice.