This paper aims to compare the traditional Binomial Tree method with advanced Neural Networks, specifically Multilayer Perceptrons (MLP), in pricing American options. The focus is on evaluating the accuracy and efficiency of these two approaches, highlighting the potential of deep learning techniques in the field of financial modeling. Results indicate that the MLP model, while requiring more computational time, offers significantly higher accuracy compared to the Binomial Tree model. This suggests substantial potential for improved financial decision-making and operational cost reduction. The paper underscores the advantages of integrating modern deep learning methods into the valuation of financial derivatives, opening avenues for future research in this area.