Graphology is a field that aims to identify, evaluate, and interpret human personality traits by analysing handwriting strokes and patterns. Handwriting can reveal various aspects of a person’s character, including emotional tendencies, fears, honesty, defences, and other personality attributes. Professional handwriting analysts, known as graphologists, are trained to interpret these patterns and can often deduce significant insights about the writer. However, the reliability of such analyses heavily depends on the analyst’s expertise and subjective interpretation. This study introduces IBM WatsonX implementation in predicting eight distinct personality traits based on handwriting features. The algorithms applied are (i) LGBM Classifier, (ii) XGB Classifier, (iii) Decision Tree Classifier, and (iv) Snap Logistic Regression. These methods are evaluated using various performance metrics, including the ROC curve, confusion matrix, and additional accuracy measures. The findings offer valuable insights, and key observations are discussed based on the performance of each algorithm.