The aim of this thesis is to design an intelligent predictive model for low-scoring competitive games. More specifically, this model integrates classical mathematical approaches with advanced computational intelligence techniques to leverage historical datasets for more accurate predictions. The thesis is divided into four main parts: The first section provides an in-depth introduction to the theoretical background, covering essential concepts in game theory and reviewing the most prominent models in the existing literature. This part lays the foundation to better understanding the challenges and methodologies used in forecasting low-scoring outcomes. The second part focuses on the creation and the refinement of the dataset.