Gas turbine systems are widely used in the power industry because they provide continuous and reliable power to the electrical grid. One of the main concerns for implementing gas turbine systems is the maintenance costs. Therefore, predictive maintenance methods driven by Deep Learning (DL) models present an opportunity to extract important information and knowledge from the process data to minimize maintenance costs and reduce equipment failure rates. A previous study aimed to benchmark various state-of-the-art DL models for predicting compressor air leak with multivariate time-series data from a modified recuperated gas turbine system. However, the brute-force approach used to select the hyper-parameters of the DL models could be improved. This paper aims to address the hyper-parameter optimization process of the best performers for predicting the next future time-step: GRU-LSTM, Sequential CNN-LSTM, and BI-LSTM. In addition, a BI-GRU model was implemented and a common grid search algorithm was combined with proposed algorithms for automating the selection of hyper-parameter values to build the DL models. The datasets were provided from experiments conducted at the U.S. Department of Energy’s National Energy Technology Laboratory (NETL) Hybrid Performance (Hyper) Facility. Results suggest better performance can be obtained from the already good performing bench-marked models; however, reproducing the best results for some models may take more training cycles. The BI-GRU model exhibited the most reproducible results across all tests.