In a constantly evolving world, the industry of insurance faces unprecedented challenges; the fundamental challenge is accurate pricing. Generalized Linear Models (GLM), have been the industry standard for many years due to their simplicity of interpretation and strong statistical foundation. GLMs, however, rely on strong assumptions about the distribution of the response variable and linearity in predictors, which can limit their capacity to model complex patterns in real insurance data. In order to mitigate these limitations, this study explores the potential of machine learning (ML) techniques—Gradient Boosting, and Neural Networks—to be used as alternative pricing tools. We examine the relative performance of GLM and various ML models using a car insurance portfolio dataset. The results clearly show that ML models consistently surpass GLM in predictive power. Although machine learning models offer improved performance, they are behind in interpretability, an area where GLMs are still ahead. To bridge this gap, the article suggests a hybrid pricing model that combines the strengths of both methods. With the integration of GLM’s analytical accuracy and ML’s flexibility and accuracy, this model sets the stage for more accurate, transparent, and adaptive non-life insurance pricing solutions.