## 1 Introduction  
In recent years, the increasing world population and rapid economic development have led to severe energy and environmental security risks, such as fossil fuel depletion and greenhouse gas emissions. Throughout the world, energy for a wide array of human and industrial endeavors has predominantly been derived from combustion processes. However, merely improving the performance level of combustion machinery to achieve low-emission goals cannot eliminate the current huge energy demand. Consequently, employing combustion processes with alternative, non-carbon-based fuels is seen as a high-impact auxiliary strategy for achieving greenhouse gas reduction targets [1; 2]. Ammonia emerges as a pivotal energy vector, having promise across various sectors for carbon-neutral fuel storage, transit, and electricity generation. The carbon-free compound presents several benefits, notably its zero-carbon characteristic, elevated energy concentration, and the advantage of an existing industrial base for its creation [3]. However, compared to fossil fuels, ammonia fuel faces challenges, such as lower laminar flame speed [4], higher ignition energy, and NO\({}_{x}\) emissions [5]. To overcome these obstacles, ammonia can be co-fired with hydrogen [6; 7]. Qualitatively, the combustion of a mixture composed of ammonia and hydrogen can enhance the rate of smooth flame propagation and reduce the minimum ignition energy [8; 9]. Ammonia-hydrogen combustion has tremendous development potential and prospects. Figure 1 shows the number of publications on ammonia-hydrogen combustion in recent years with data sourced from the Web of Science; thus, this paper's research on predicting the laminar velocity of ammonia-hydrogen has significant importance. The laminar burning velocity (LBV) is a fundamental and crucial characteristic of fuel combustion, which provides an indication of the overall reactivity of the fuel, plays a critical role in the estimation of the thermal energy emission rate, and verifies both detailed and reduced combustion reaction mechanisms. Currently, the main methods for measuringLBV can only be measured experimentally at low pressure and temperature, and the traditional experimental methods for deriving LBV under different conditions for various fuels are very cumbersome. Nevertheless, despite their limitations, the experimental measurements of LBV continue to serve as a primary benchmark for verifying the accuracy of different fuel combustion mechanisms [11; 12]. An alternative approach for determining LBV involves employing computational tools like Cantera, Chemkin, and OpenSMOKE++, which utilize chemical kinetics mechanisms to calculate LBV using one-dimensional simulation methods. In the realm of engine simulation, applying comprehensive combustion models to both zero-dimensional (0D) and one-dimensional (1D) models presents significant computational challenges and is often deemed impractical due to the high demand on processing power. Machine learning approaches can be leveraged to expedite the computation of LBV within both 0D and 1D engine simulation models, without compromising the accuracy of the results [13]. Machine learning methods have been applied to predict the laminar burning velocity and to study the dependence of LBV on various factors. Some studies have used Support Vector Machine (SVM) and Artificial Neural Network (ANN) methods to compare the prediction of LBV for low-carbon fuels, with results indicating that ANN methods performs better with single fuels, while the opposite is true for mixed fuels [14]. [PERSON] [15] selected a squared exponential Gaussian Process Regression (GPR) model for predicting the laminar burning speed of single hydrocarbons and oxygenated fuels. [PERSON] [16], by comparing different ML models, recommended using GPR and Neural Network (NN) algorithms to predict the LBV of NH\({}_{3}\)/H\({}_{2}\)/Air.