The Black-Scholes equation is a cornerstone of modern finance, widely used for option pricing. However, the stochastic and dynamic nature of today's financial markets exposes the limitations of classical analytical solutions. This paper investigates the application of deep learning methods, including Physics-Informed Neural Networks (PINNs), Fourier Neural Operators (FNOs), and Transformers, to solve the Black-Scholes equation. We compare their performance in terms of accuracy, scalability, and robustness to noise, demonstrating that these methods outperform traditional approaches and offer more suitable solutions for modern financial markets. Our results also suggest that a combination of these techniques could lead to significant advances in financial modeling. This study opens new perspectives for researchers seeking robust solutions for stochastic financial systems.