Gold, a highly valued and sought-after asset, has long captured the interest of investors, financial analysts, and policymakers due to its historical significance. To predict future gold prices, this study uses the Generalised Autoregressive Conditional Heteroskedasticity (GARCH) model. The GARCH model is implemented in R Studio, and this study’s findings provide valuable insights for investors, equipping them with informed decision-making capabilities and robust risk management strategies. The precise gold price forecasting achieved through the GARCH method highlights its reliability in financial prediction. This research also adds to the body of knowledge already available on gold price forecasting and provides a basis for investigating alternative models and incorporating macroeconomic aspects to improve prediction accuracy. Three versions of GARCH model are compared namely: GJR-GARCH, EGARCH, and standard GARCH. The implications shows that EGARCH model showed promising results, there is scope for further research and validation. Continual refinement and testing of the model’s assumptions and parameters are essential to ensure its reliability and applicability in different market conditions.