<p>Trading on the stock market plays a prominent role in the financial industry, with stock prices determined by the interaction of supply and demand. Investors continuously seek methods to predict market patterns and minimize losses despite the inherent volatility of stock prices. Predicting stock prices using machine learning and artificial intelligence is one promising approach. While stock market fluctuations are inevitable, leveraging AI to make informed forecasts is both feasible and recommended. With the advancement of machine learning, various algorithms have been developed to forecast stock prices. This study introduces the Battle Royal Optimizer and Extreme Learning Machine model to predict stock prices. The dataset spans from January 2, 2015, to June 29, 2023, collected from the Hong Kong stock market. Our findings suggest that the proposed model offers a valuable approach to analyzing and forecasting stock time series data. When compared to alternative models, it demonstrates superior prediction accuracy and goodness of fit. This model holds great potential for financial market analysis and can significantly benefit market participants.</p>