<p>Gold price forecasting is simply predicting the gold price using various historical data and analysis to help with investment decisions and market strategies. This research presents an innovative approach to forecasting gold prices using a hybrid Markov weighted Fuzzy Intuitionistic Crayfish Time series (MFICT). The gold price dataset was compiled through extensive data gathering processes. The proposed model incorporates intuitionistic fuzzy sets and fuzzy logic to improve on classical time series forecasting, which is unable to manage financial data imprecision and uncertainty adequately. The model capacity to identify shifting market patterns is enhanced by the Markov weighting technique. For better forecasting accuracy, the proposed model combines weighted fuzzy time series, intuitionistic fuzzy c-means clustering, and fuzzy time series. The Markov Weighted Intuitionistic Fuzzy Time Series model parameters are optimized using the optimization algorithm. By modifying weights and honing solutions, the optimization improves the exploration and exploitation stages while guaranteeing a comprehensive search and increased prediction accuracy. By using the center of gravity method for adjusting the forecasts with fuzzy relational equations, this innovative approach overcomes the limitations of traditional time series models, providing greater accuracy and flexibility in gold price forecasting. The proposed model achieves a Mean Absolute Error (MAE) of 12.82% and a Mean Square Error (MSE) of 3.93%, demonstrating superior forecasting accuracy.</p>