This paper proposes a machine learning approach to profile financial knowledge among the Portuguese population. Given the increasing complexity of the financial sector and the growing digitalization of customer attendance, understanding individuals’ financial knowledge is crucial for informed decision-making. The study aims to identify which demographic and behavioral factors influence individuals’ positive or negative levels of financial knowledge. A supervised learning classification model is employed, utilizing algorithms such as Naïve Bayes, Logistic Regression, Support Vector Machine, Artificial Neural Network, and K-Nearest Neighbors. The model’s performances are evaluated using metrics like classification accuracy, precision, recall, and F1-score. The methodologies employed demonstrated high efficiency, achieving a classification accuracy and recall of 78.5%, and precision of 61.6%, with the best model, Naïve Bayes, identifying academic education (with individuals holding degrees in business and economics sciences) and male individuals as the two most influential features in predicting positive financial knowledge, with a probability of 84% and 83%, respectively. By understanding the factors influencing financial knowledge, the study contributes to developing targeted strategies for improving financial knowledge and empowering individuals to make informed financial decisions.