The increasing transformation of the European energy market, driven by the rise of intermittent renewable energies, the decommissioning of controllable power plants and dependence on short-term storage, poses challenges for assessing security of supply. In order to evaluate the capacity of available generation to meet demand in uncertain conditions, market model optimizations using the Monte Carlo (MC) approach are employed. However, the high computational costs of this approach limit assessment resolution. This paper investigates metamodeling as a strategy to reduce these computational costs. Metamodelling is a process of using mathematical models on a subset of simulations to map outcomes to the input data. This reduces the total number of simulations required. The study explores three key steps: enhancing input-output correlation, identifying effective machine learning (ML) models and selecting optimal training samples. While no single model performs adequately due to data complexity, a two-model pipeline significantly improves prediction accuracy. An active learning approach is also introduced to further optimize sample selection. The results show that training on only twenty percent of the data reduces the computation time by more than 75%, with a relative error below 10%. These findings demonstrate the potential of metamodeling to enable efficient, high-resolution resource adequacy assessments in evolving energy systems.