Accurately pricing options remains a fundamental challenge in quantitative finance due to market complexities such as stochastic volatility and irregular price movements. Traditional models like Black-Scholes struggle to incorporate these real-world dynamics. This paper introduces a hybrid deep learning framework combining Physics-Informed Neural Networks (PINNs) and Fourier Neural Operators (FNOs) to enhance predictive accuracy and computational efficiency. Our hybrid model reduces Mean Squared Error (MSE) by up to 20% compared to standalone PINNs and FNOs. We validate the model using synthetically generated financial data, considering extreme market conditions such as high volatility and short maturities. Finally, we discuss the limitations of synthetic data and propose future work incorporating real financial datasets. The results demonstrate the potential of this approach for real-time option pricing and risk management applications.