Subjective assessment of craniosynostosis severity often leads to inconsistent treatment outcomes. This study introduces and evaluates innovative quantitative methods, including distance-based, spectral, shape descriptor, and deep learning view-based approaches, to objectively assess the severity of sagittal craniosynostosis. The effectiveness of these quantitative scores is determined by their correlation with expert clinical ratings, with the goal of improving the consistency and accuracy of severity evaluations. Furthermore, we applied the methods developed with synthetic data to real clinical datasets, focusing on pre- and post-surgery severity assessments. This study showcases how our quantitative approach effectively guides and optimizes surgical treatments, underscoring its potential utility in clinical practice.