This paper introduces a novel importance sampling technique for option pricing that leverages feedforward neural networks to approximate the optimal change of measure. The key idea is to train a neural network to map the underlying asset prices to a control variate that minimizes the variance of the Monte Carlo estimator. The authors provide theoretical analysis showing that the neural network can approximate the optimal importance sampling density arbitrarily well given sufficient capacity. The method is applied to price various options including European, Asian, and barrier options under different asset dynamics. Numerical experiments demonstrate that the neural network-based approach achieves substantial variance reduction, often by several orders of magnitude, compared to standard Monte Carlo and traditional importance sampling methods. The computational cost of training the network is offset by the significant reduction in the number of simulation paths required for accurate pricing. The method is particularly effective in high-dimensional settings where traditional importance sampling techniques struggle. Overall, the paper presents a practical and efficient framework for variance reduction in option pricing that can be easily integrated into existing Monte Carlo simulation pipelines.