Forecasting stock prices is a complex and challenging task due to the volatile and unpredictable nature of financial markets. Traditional models often struggle with real-time data integration, precise sentiment analysis, and adaptability to dynamic market conditions. This paper introduces the IntelliFusion Adaptive Decision Engine (IADE), a comprehensive hybrid model integrating advanced technologies such as Deep Q-Learning (DQN), Prophet Algorithm, Bidirectional Encoder Representations from Transformers (BERT), Adaptive Resonance Theory Neural Network (ART-NN), and Transformer-based models with attention mechanisms. IADE aims to enhance user-friendliness, improve real-time forecasting accuracy, refine sentiment analysis precision, and provide adaptive predictive capabilities. The proposed system effective in forecasting performance and decision-making effectiveness in volatile financial environments.