As part of the tea manufacturing process, fermentation plays a significant role in determining the quality of the tea. An overall rating of tea quality is determined by factors such as brightness, body, taste, etc. In order to get preferred quality tea, it is essential to maintain process parameters like temperature and relative humidity at an optimal level. One of the major quality attributes of tea is brightness, and it is affected by the fermentation conditions. In this paper, the effects of fermentation conditions on tea liquor brightness are discussed. By using network-based instruments installed in a tea factory, process parameters are collected during fermentation. The corresponding tea quality along with its different quality attributes were collected from tea taster. For the study, process parameters were used as inputs, and brightness was used as an output. Firstly, the multivariate linear regression (MLR) method was adopted and 55.85% correct correlation was found for training data and 55.94% for validation data. This study found that the root mean square error (RMSE) for both training and validation is 0.2. After that, artificial neural network (ANN) was implemented to solve the stated problem. Models based on the back propagation-multilayer perceptron (BP-MLP) showed 95.53% correct correlation for training, and 95.47% for validation. RMSE found in this method is 0.079 for both training and validation. Therefore, the performance of the ANN model demonstrates how fermentation conditions affect tea liquor brightness as well as its potential for standardizing it numerically.