###### Abstract  
In recent years, machine learning algorithms have been applied in many real-time applications. Crises in the energy sector are the primary challenges experienced today among all countries across the globe, regardless of their economic status. There is a huge demand to acquire and produce environmentally friendly renewable energy and to distribute and utilize it efficiently because of its huge production cost. PEMFC are known for their energy efficiency and comparatively low cost, and can be an alternative energy source. The efficiency of these PEMFC can still be enhanced with the help of advanced technologies like machine learning and artificial intelligence, as they provide an optimal solution to explore the hidden knowledge from the generated data. The proposed model attempts to compare several design techniques with varied humidity levels. To enhance the performance of PEMFC, the various humidification processes were considered during the experimental study. The humidification reduces the heat during energy generation and increases the performance of PEM fuel cell. The humidity levels such as 100%, 50%, and 10% were considered to be tested with the machine learning models. The SVMR, LR, and KNN algorithms were tested and observed with the RMSE value as the evaluation parameters. The observed results show that SVMR has an RMSE rate of 0.0046, the LR method has an RMSE rate of 0.0034, and KNN has an RMSE rate of 0.004. The analysis shows that the LR model provides better accuracy than other models. The LR model enhances the PEMFC performance. P 2022 2020 September 2022 20  
computational fluid dynamics (CFD). A very small amount of deviation was reported in most of the previous works between experimental and numerical (CFD) analysis; hence, it can be inferred that CFD is a very viable method of predicting and analyzing the performance of a PEMFC having a high accuracy percentage [4]. The main factor to be considered when designing a fuel cell is to ensure proper humidification without causing flooding as expressed in previous literature [5]. Flooding factors are considered when designing the flow channels and the operating conditions of the fuel cells. The PEMFC needs to be humidified for its proper functioning and prevent it from drying out. This humidification needs to be provided through an anode as well as a cathode inlet. However, higher humidification, especially for the cathode can result in flooding conditions, which in turn will lead to the deterioration of the performance of the fuel cell. Fuel cells are gaining worldwide importance because of their clean nature, fuel, and reliability; they are finding increased applications in almost every industry. Proton exchange membrane fuel cells (PEMFCs) are preferred on a larger scale due to their lower operating conditions and high power output. Increased manufacturing costs for fuel cells have been a major concern for researchers around the world. Scaling up PEMFC can help reduce this very cost to a great extent. As found in the literature, an enormous number of studies have been done in the field of PEMFCs with a typical active area of 25 to 50 cm\({}^{2}\). The authors have found from the literature that there are not many studies that deal with scaled-up PEMFC with active areas of 100 cm\({}^{2}\). The power output of a single cell is increased as the PEMFC is scaled up. On the other hand, if the PEMFC is scaled up, it must be ensured that the flow channel is properly designed for effective water and species distribution; otherwise the power density would drop as compared to the standard PEMFC with an active area of 25 cm\({}^{2}\). Hence, this present work will contribute significantly to the existing literature by giving an in-depth analysis of scaled-up PEMFC and all other factors which affect its performance by evaluating some novel flow-field designs that numerically use CFD. A real-time numerical data analysis was implemented by [6] to enhance the performance of PEMFC with various designs.