Dementia is a progressive neurodegenerative condition affecting millions worldwide, highlighting the need for early and accurate detection. This study leverages the Aiginition Longitudinal Biomarker Investigation of Neurodegeneration (ALBION) dataset, integrating cognitive, psychological, physical, socio-demographic, among others, to enhance early diagnosis. In this direction, two approaches are proposed: the Always-Measured Model, which uses a limited set of consistently recorded features, and the Voting-Based Hybrid Model, which utilizes the dataset’s full multimodal and longitudinal scope. While the Always-Measured Model exhibited bias toward the Normal Cognition class (MCC = 0.64), the first-visit model within the ensemble achieved an MCC of 0.83. This demonstrates that initial-visit data alone can enable accurate detection. Additional data from follow-up visits did not improve the performance of the ensemble approach. However, the ensemble proved valuable in high-certainty cases (85.42% of instances), achieving an MCC of 0.94 and showcasing high robustness and accuracy.