Weather forecasting is essential to disaster preparedness, aviation safety, agriculture, and long-term climate planning. The traditional Numerical Weather Prediction systems (NWP) rely on physical equations and formulas. Recently, artificial intelligence-based forecasting models have emerged, using large historical data sets and inductive reasoning instead. This project investigates the strengths and limitations of AI-based weather models compared to traditional NWP systems. By comparing forecast accuracy across different variables such as temperature, precipitation, and extremity, I investigate where the AI models outperform and underperform the NWP systems — notably long-range stability and in rare events. I will also examine how the strengths of both can be combined and best integrated in hybrid models.