Plasma AT(N) biomarkers—measuring amyloid, tau, and neurodegeneration markers—demonstrated high diagnostic accuracy in differentiating Alzheimer's disease from frontotemporal lobar degeneration across diverse Latin American populations. Machine learning algorithms integrated with neuroimaging data achieved clinically meaningful precision in this challenging diagnostic distinction. This represents a significant advancement in dementia diagnosis, particularly for populations where invasive cerebrospinal fluid testing or expensive PET imaging may be less accessible. The ability to distinguish these two major dementia types through simple blood tests addresses a critical diagnostic gap, as both conditions can present with overlapping symptoms but require different management approaches. Previous biomarker research has been heavily skewed toward populations of European ancestry, making this Latin American validation especially valuable for global health equity. The diagnostic accuracy achieved here could transform early detection capabilities in regions with limited neurological infrastructure. However, the technology's real-world implementation will depend on cost-effectiveness and standardization across laboratories. While promising, these findings need replication in other diverse populations and longitudinal validation to confirm sustained accuracy over disease progression timelines.