Accurate option pricing is critical for the effective functioning of financial markets, providing traders and investors with the means to hedge risks and capitalize on market movements. Traditional models such as the Black-Scholes, Binomial Tree, Trinomial Tree, Monte Carlo Simulation, and the Garman-Kohlhagen model have long been the standard for option pricing. However, these models often face limitations in capturing market complexities and extreme events. We propose here a hybrid approach that combines Genetic Algorithm (GA) optimization with Backpropagation (BP) neural networks to enhance the precision of option pricing. It uses HS300 index stock data from 2013 to 2022, including stock prices, volumes, and price changes. The hybrid GA-BP model is tested for its ability to make more accurate price predictions. The model helps investors make better decisions by improving pricing strategies and managing risks effectively. The Hybrid GA-BP neural network model leverages the global search capabilities of GA to optimize the initial weights and biases of the BP neural network, thereby avoiding local minima and improving convergence rates. This integrated model is trained and tested on historical market data, with its performance benchmarked against traditional models. Empirical results demonstrate that the Hybrid GA-BP neural network model significantly outperforms traditional models in terms of pricing accuracy. The model shows superior precision when comparing actual market prices with predicted prices, reducing errors and increasing reliability. This enhancement in pricing precision can lead to more informed trading decisions and better risk management strategies. The findings of this research contribute to the growing body of knowledge in financial engineering by showcasing the potential of hybrid machine learning approaches in financial modeling. The Hybrid GA-BP neural network model presents a promising tool for practitioners and researchers aiming to improve option pricing methodologies in increasingly complex financial markets.