This article discusses advanced artificial intelligence (AI)-based strategies for the design and personalization of three-dimensionally (3D) fabricated hand exoskeletons, with a focus on adaptive, data-driven methodologies. It highlights the crucial role of intelligent personalization in improving user comfort, functional performance, and rehabilitation outcomes, particularly in medical and care settings. The proposed approach integrates biomechanical modeling, high-resolution 3D scanning, and machine learning (ML) algorithms to create exoskeleton systems tailored to the unique anatomical and motor characteristics of individual users. This article presents both a theoretical framework and practical implementation of AI-based adaptation, addressing key challenges such as precise anatomical fit, ergonomic optimization, and real-time responsiveness. Specific emphasis is placed on AI-based feedback mechanisms that enable continuous, dynamic adjustment of control parameters during device operation. Case studies illustrate the effectiveness of these techniques in improving performance and rehabilitation progress for individual users. By combining intelligent modeling, adaptive control, and additive manufacturing, this research advances the field of wearable robotics and points the way to more accessible, efficient, and fully personalized assistive technologies.