This thesis investigates the application of machine learning (ML), particularly Convolutional Neural Networks (CNNs), for the development of reduced-order models (ROMs) aimed at predicting key thermochemical quantities in laminar premixed hydrogen-air combustion, characterized by intrinsic flame instabilities. The primary objective is to assess whether data-driven methods can serve as accurate and efficient surrogates for high-fidelity numerical simulations, supporting ongoing efforts in clean energy research and hydrogen utilization. The methodology is based on a two-stage approach: an a priori phase and an a posteriori phase. In the a priori stage, CNNs are trained on Direct Numerical Simulation (DNS) data to learn mappings from input fields that can be the progress variable alone or in combination with the mixture fraction $Z_{mix}$, to target thermochemical quantities, including source terms and transport properties.