Purpose The main reasons for modeling and predicting volatility with GARCH models are the capturing of time-varying volatility in financial time series like returns on stocks, foreign currency prices, and commodity prices, and their prediction. There are periods of high volatility as well as low volatility which are not constant across different time periods in a financial market. Such dynamic behaviour can be quantified using GARCH models by accounting for the fact that large price movements are typically followed by further large movements of either direction, while small movements follow small ones. In this regard, GARCH models are critical in the risk management, option pricing, portfolio optimization, and financial decision-making contexts. They help practitioners and researchers to make better predictions for future market conditions, appraise possible risks, and design strategies to hedge adverse movements in the market more efficiently. Design/Methodology/Approach Design and methodology of modeling and forecasting volatility using GARCH models. The GARCH models capture the conditional heteroskedasticity within a time series model that needs to be estimated. This commences by determining the proper variant of the GARCH, GARCH(p,q), EGARCH, or TGARCH, that should be implemented depending on the nature of the data. The values of the parameters for the estimated model are derived from maximum likelihood estimation using historical financial data. The residual analysis and likelihood ratio tests are used in model diagnostics checks, thereby ensuring that the model is adequate. This fitted model is then used to predict future volatility for risk evaluation and financial decision-making. The method focuses on capturing volatility clustering and time-varying variance in financial time series Findings The results of modelling and forecasting volatility with GARCH models typically show significant volatility clustering and time-varying variance in financial time series. GARCH models capture these patterns well and therefore provide accurate representations of conditional heteroskedasticity. Results often point to the fact that past volatility and shocks affect the levels of current volatility, supporting the persistent nature of volatility in financial markets. Further, asymmetric GARCH variants like EGARCH or TGARCH are proven to the effect that the negative shock can be more volatile in nature than positive shocks. Overall, it is observed that GARCH models become a robust tool for forecasting volatility, improvement in risk management, and financial decision-making processes.