The growing population of people living with dementia demands innovative architectural solutions that prioritize wellbeing. Floor layouts significantly affect the quality of life for people living with dementia, but the nuances of perceived user experience remain a challenging criterion to evaluate during the early design stages. Visual sightlines are one of the key aspects for dementia-inclusive design where machine learning (ML) provides decision-support means to validate early-stage designs. In this study, an isovist-based quantification scheme is proposed to capture visual access data to evaluate the extent of compliance of floor layouts with respect to dementia design principles (DDP). The visual access quality labels are determined by the number of isovists that satisfy the visual access requirement for their respective DDPs, thereby ensuring a consistent and objective measurement of visual access. 18 unique spatial features were tested using feature filtering methods to predict visual access quality labels. The framework bridges qualitative design principles with quantitative methods, introducing a scalable approach for evaluating visual access in the context of dementia-friendly design. The ML model was evaluated using individual class output and multi-output evaluation metrics based on 7 feature inputs and 2 class outputs, achieving 84–87% accuracy on an individual class and 72% on the subset accuracy metric. The results suggest a viable pathway for developing ML support tools to provide feedback on DDP compliance at an early design stage.