In traditional neural networks applied to regression tasks, the output layer typically produces a single value for each prediction. This singular output approach limits the model's ability to quantify the uncertainty associated with its predictions. To address this limitation, MVE method is introduced, which assumes a specific distribution for the predictions, characterized by a set of parameters derived from the output layer. For instance, by adopting a Gaussian distribution assumption, MVE can articulate the variability in predictions through both the mean and standard deviation. This dual-output approach allows for UQ in ML. The optimization of these parameters is achieved through the use of a Negative Log Likelihood (NLL) loss function, employing maximum likelihood estimation to refine the mean and standard deviation values within each training iteration. Furthermore, to ensure the calculated variance remains non-negative, a softplus function is applied. This methodological advancement enriches neural network outputs with valuable uncertainty information, enhancing their applicability and reliability in predictive tasks.