Prediction of the stock market index is important for investors and financial analysts to mitigate risks and achieve profits. Multilayer perceptron is an efficient neural network to learn non-linear relationships for stock price prediction with high accuracy. FTSE Bursa Malaysia KLCI (FBM KLCI) represents the economic performance of Malaysia. Therefore, it is important to monitor the market sentiment based on FBM KLCI. Stock price prediction becomes a strident challenge for the investors and financial analysts to mitigate risks and achieve profits. This research aims to predict the closing prices of FBM KLCI with neural networks comprisingtwo hidden layers. The data consists of historical stock data, including volume, opening, closing, and low and high prices from January 2019 to May 2025. Model comparison is performed using random forest (RF). The model performances are evaluated with the coefficient of determination (R²), root mean squared error (RMSE), and mean absolute error (MAE). The result of this study shows that the neural network is more stable and consistent in predicting the next closing prices of FBM KLCI. This study is significant because it contributes to the field by offering an efficient method for predicting stock index prices, which is expected to guide investors and fund managers in their decision-making.