Forecasting the movement of global stock indices’ value is a critical aspect of financial decision-making and risk management. This research paper explores an innovative approach to enhance the accuracy of predicting price movements, with a focus on direction rather than precise values. Leveraging novel architecture, this approach integrates soft computing techniques, including Adaptive Neuro-Fuzzy Inference Systems, Fuzzy Logic, and hybrid methods, to develop robust machine learning models for stock price movement forecasting. The study not only demonstrates the potential for improving forecasting accuracy but also outlines future research prospects and enhancements. These prospects include extensive testing using large datasets, incorporating additional technical indicators to refine trading strategies, enhancing the dataset itself, and exploring diverse architectural setups. Furthermore, the paper explores the application of various algorithms to further enhance prediction performance. To anticipate future price movements more effectively, the research suggests collecting a more extended historical dataset for investigating techniques such as particle swarm optimization and ant colony optimization.