The automotive industry is under increasing pressure to develop cleaner and more efficient technologies in response to stringent emission regulations. Hydrogen-powered internal combustion engines represent a promising alternative, offering the potential to reduce carbon-based emissions while improving efficiency. However, the accurate estimation of in-cylinder pressure is crucial for optimizing the performance and emissions of these engines. While traditional simulation tools such as GT-POWER are widely utilized for these purposes, recent advancements in artificial intelligence provide new opportunities for achieving faster and more accurate predictions. This study presents a comparative evaluation of the predictive capabilities of GT-POWER and an artificial neural network model in estimating in-cylinder pressure, with a particular focus on improvements in computational efficiency. Additionally, the artificial neural network is employed to predict the equivalent flame radius, thereby obviating the need for repeated tests using dedicated high-speed cameras in optical access research engines, due to the resource-intensive nature of data acquisition and post-processing. Experiments were conducted on a single-cylinder research engine operating at low-speed and low-load conditions, across three distinct relative air–fuel ratio values with a range of ignition timing settings applied for each air excess coefficient. The findings demonstrate that the artificial neural network model surpasses GT-POWER in predicting in-cylinder pressure with higher accuracy, achieving an RMSE consistently below 0.44% across various conditions. In comparison, GT-POWER exhibits an RMSE ranging from 0.92% to 1.57%. Additionally, the neural network effectively estimates the equivalent flame radius, maintaining an RMSE of less than 3%, ranging from 2.21% to 2.90%. This underscores the potential of artificial neural network-based approaches to not only significantly reduce computational time but also enhance predictive precision. Furthermore, this methodology could subsequently be applied to conventional road engines exhibiting characteristics and performance similar to those of a specific optical engine used as the basis for the machine learning analysis, offering a practical advantage in real-time diagnostics.