Financial forecasting in the US stock market has traditionally relied on econometric models such as ARIMA, SARIMA, and GARCH, which offer interpretability and robust performance in stable environments. However, the increasing complexity and volatility of modern markets— driven by nonlinear dynamics and high-frequency trading—have exposed the limitations of these classical approaches. This research aims to evaluate and compare the predictive performance of traditional econometric models and AI-augmented methods, with a special focus on the Prophet model, in forecasting stock prices and volatility for major US firms, specifically Apple (AAPL) and Microsoft (MSFT). The study seeks to determine whether hybrid AI-econometric frameworks provide superior accuracy and risk quantification compared to standalone models. Historical daily price data (January–June 2024) from Yahoo Finance underwent preprocessing: log-return transformation, stationarity enforcement (ADF/PP tests), outlier winsorization, and volatility clustering validation. Models were trained on 80% of the data (105 observations) and tested on 20% (26 observations). Performance was measured via RMSE, MAE, AIC/BIC, and uncertainty interval accuracy. Prophet outperformed traditional models, reducing Apple’s RMSE by 6% (7.02 vs. 7.46) and MAE by 8.9% (4.70 vs. 5.16) compared to AI-augmented ARIMA. For Microsoft, Prophet achieved 11% lower RMSE (9.46 vs. 10.64) and 14.4% better MAE (5.89 vs. 6.88). AI-augmented GARCH improved...