Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by challenges in social interaction, communication, and repetitive behaviors. Traditional approaches to diagnosing and understanding personality traits in individuals often face limitations such as subjectivity, delayed detection, and lack of personalization. Recently, AI-driven fingerprint-based personality prediction systems have gained attention as a promising tool for more precise assessment and intervention strategies. Techniques such as electroencephalography (EEG) are commonly used to capture and process neural signals, providing real-time insights into brain activity. These insights are crucial for early and accurate prediction of personality traits and for identifying related anomalies. Interventions leveraging AI-driven fingerprint-based systems, including neurofeedback training, interactive virtual reality modules, and emotion recognition tools, show potential in enhancing attention, emotional regulation, and social communication. This study also addresses challenges such as ethical considerations, technological limitations, and the need for interdisciplinary collaboration in deploying these technologies. The integration of machine learning, multimodal bioimaging, and wearable personality prediction systems points toward a future where AI-driven tools play a pivotal role in delivering personalized and accessible care for individuals with ASD.