The article explores various methods for estimating uncertainty in neural network predictions, focusing on the challenges of creating efficient prediction intervals without extensive computational demands. It evaluates different techniques, including MC Dropout, Mean-Variance Estimation, and Quantile Regression, to determine their effectiveness in providing accurate prediction intervals. The Mean-Variance method, which involves training two separate models for mean and variance estimation, is highlighted for producing the narrowest intervals, albeit with a lower Prediction Interval Coverage Probability (PICP) score. In contrast, MC Dropout and Quantile Regression yield wider intervals but achieve higher PICP scores. The article underscores the importance of balancing interval width with coverage probability, emphasizing that the choice of method depends on specific application requirements. Additionally, it suggests that integrating these techniques into existing models enhances prediction reliability by offering uncertainty estimates.