Indian Stock Market Volatility Forecasting and Emerging Markets focusing case studies sequentially brings innovative models to evolve in emerging economics paradigms. This IPL matches mark the growing change which small to medium size companies wishes to boost efficiency to compete into Indian stock market euphoria much similar like boosting to western proven models. Following the comparison of hybrid econometric models deep learning algorithms with advanced models such as GARCH(1,1), EGARCH(1,1) one could suggest that predicting declined volatility trends is more complex than risen trends and requires more micromodels. This observation within the scope of HFT, arbitrage trading suggests a lack of performance evaluation algorithms in regards to casual dependency relationships between volatility clusters and performance metrics. The turned GARCH-LSTM and EGARCH-CNN models based upon Nifty 50 daily closing prices between January 2013 to December 2024 were later analysed with a few converging auxiliary metrics where further evaluation was performed on RMSE, MAE, MAPE defined test set. The results show that EGARCH-CNN outperformed with RMSE, MAE and MAPE values of 0.00755, 0.00562, and 4.91% respectively whereas GARCH-LSTM recorded an RMSE of 0.00812, MAE of 0.00607 and MAPE of 5.24%. It is also clear from visual examinations that GARCH LSTM has a lower performance when it comes to capturing volatility movements during crisis periods, for instance, throughout the COVID 19 pandemic. This illustrates the additional value the hybrid approach contributes in capturing both linear and nonlinear volatile shifts. This information is important for active portfolio managers in emerging markets, as well as for managers dealing with risks and other financial strategic decisions.