This paper investigates a novel approach to personality prediction through graphology, combined with artificial intelligence (AI), as a tool for improving recruitment processes and tackling high attrition rates in modern organizations. Using a dataset of 5130 handwritten samples from subjects aged 18 to 67, this study focuses on predicting the “Big Five” personality traits: Agreeableness, Conscientiousness, Extraversion, Neuroticism, and Openness. A convolutional neural network (CNN) model trained on this data achieved an accuracy of 74%, showcasing the potential for precise personality analysis based on handwriting patterns. This AI-driven graphological analysis can serve as a preliminary screening tool in recruitment, particularly identifying candidates prone to Neuroticism, which may correlate with mental health concerns and higher turnover risk. The performance metrics for this class shows moderate precision (0.51), high recall (0.78), and reasonable F1-score of 0.62, which indicates that the model is moderately effective in distinguishing Neuroticism from other traits as desired. Candidates who demonstrate positive indicators in traits associated with workplace stability like Agreeableness, Extraversion and Openness having high precision of 0.94, 0.95, and 0.80, respectively, and high specificity of 4.33, 9.18, and 5.63, respectively, are advanced in the hiring process. By integrating personality prediction into recruitment, organizations can better align new hires with role demands and organizational culture, fostering a workforce with higher engagement and resilience. The proposed approach addresses the critical challenges of employee retention and attrition, offering a proactive strategy for building a more stable, motivated, and cohesive workforce.