Weather forecasting is vital for sectors such as disaster response, agriculture, and risk management, and even influences daily decision-making. Reliable forecasts reduce potential losses, strengthen preparedness, and help in planning routine activities. In recent years, artificial intelligence techniques—particularly deep learning—have become more prominent for improving prediction accuracy. This paper reviews the application of AI-based models in weather forecasting and compares them across several technical aspects. Models are evaluated based on their accuracy, computational needs, and suitability across different climatic regions. The study also points out key challenges in data balance, resource use, and model transparency. Overall, it provides an updated outlook on AI-assisted forecasting and discusses ways to develop more efficient, reliable, and interpretable prediction systems.