Engine ECU calibration is costly, time-consuming, and requires expert effort. Traditionally, for each operating point (fixed speed × load) engineers test many input–parameter combinations, measure the output, and iteratively retune an approach that becomes impractical as the number of parameters grows. This thesis investigates the potential of Machine Learning to develop new control-calibration methods, with the aim of drastically reducing calibration effort and improving robustness in real-world operations. Premise. With surrogate models, the primary objective is to learn a predictor with high accuracy across the entire admissible input domain while using as few training points as possible. Achieving this reduces physical engine tests and there- fore development time and cost.