Background: Falls are a leading cause of injury, disability, and mortality among older adults, imposing significant burdens on individuals and healthcare systems worldwide. Effective fall risk assessment and intervention strategies are critical for improving mobility and preventing adverse health outcomes in aging populations. Traditional assessment methods often require specialized equipment or trained personnel, limiting accessibility and scalability. Objective: This study investigates the effectiveness of an artificial intelligence (AI)-assisted dynamic postural control screening system in predicting and mitigating fall risks among older adults. By integrating AI-driven image analysis with standardized mobility assessments, the study aims to enhance early detection and personalized intervention strategies for fall prevention. Methods: A cohort of elderly participants underwent AI-assisted postural control screening, which incorporated convolutional neural networks (CNNs) with the Short Physical Performance Battery (SPPB) to evaluate balance, gait, and lower limb strength. Participants were divided into an experimental group receiving AI-driven personalized exercise interventions and a control group following standard care. Mobility outcomes, including SPPB scores, gait speed, and sit-to-stand performance, were measured pre- and post-intervention. Results: Our findings suggest that AI-assisted postural control screening effectively enhances mobility in elderly individuals. The experimental group exhibited a 7% increase in SPPB scores, alongside notable improvements in gait speed (+13%) and sit-to-stand performance (-13%). These results indicate that real-time AI feedback may play a critical role in optimizing exercise interventions, as personalized recommendations can be dynamically adjusted based on individual progress. Notably, participants with higher adherence to AI-driven programs showed greater functional gains, underscoring the importance of continued engagement in technology-assisted fall prevention strategies. Conclusions: AI-driven dynamic postural control screening presents a scalable, cost-effective solution for fall risk assessment and prevention in older adults. While the technology shows promising outcomes, challenges such as accessibility, validation across diverse populations, and implementation in low-resource settings remain. Future research should explore long-term effects, broader demographic inclusion, and strategies to overcome technological barriers to enhance widespread adoption in geriatric care.