This paper introduces an exhaustive methodology for analyzing and forecasting stock market trends by leveraging fundamental and technical analysis methods, combined with deep learning models. The dataset used encompasses real-time stock data from prominent companies such as Google, Microsoft, Apple, and Amazon, comprising stock attributes. Initially, the analysis delves into exploring the distribution and correlation between open and closed prices, offering insights into the market dynamics. Employing visualization techniques, we scrutinize the attributes across the datasets, with a specific focus on comparing high and close prices, elucidating potential patterns and trends. Furthermore, this project delves into uncovering underlying trends and seasonality within the dataset, providing invaluable insights for investors. Our methodology combines both fundamental and technical analysis to provide investors with the essential tools for making investment decisions. In the forthcoming sections, we will employ specialized RNN architectures, specifically Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models, to forecast stock market behavior. By conducting a meticulous comparative analysis, we aim to elucidate the effectiveness of these models in predicting stock market trends.