Weather forecasting is a complex and challenging task due to the inherent chaotic nature of atmospheric processes. Chaos theory offers valuable insights into the underlying dynamics of weather systems, providing both deterministic and stochastic methods for prediction. This paper evaluates the effectiveness of these methods in weather forecasting, focusing on their ability to capture the non-linear behavior of atmospheric phenomena. Deterministic models aim to forecast future states of the atmosphere using precise initial conditions and deterministic equations, while stochastic models introduce randomness to account for uncertainties. Through a comparative analysis, this study assesses the strengths and limitations of each approach and discusses their potential for improving weather forecasting accuracy.