Reliable estimates of population density are essential for the conservation of apex predators such as the jaguar (Panthera onca), particularly in peripheral regions of their distribution where existing data are insufficient to guide effective management. In Mexico, northeastern landscapes remain underrepresented in jaguar research, limiting the development of context-specific conservation strategies. To address this gap, we conducted a camera trap survey in the El Cielo–Sierra de Tamalave biological corridor, a transitional zone located at the northeasternmost limit of the species’ range. Over a 91-day sampling period, we deployed 104 cameras across 52 paired stations and applied a random thinning spatial capture–recapture model (rt-SCR), which integrates both identified and unidentified photographic detections. This represents the first application of rt-SCR to jaguar data in Mexico. The model yielded a density estimate of 1.29 (0.93–1.70) individuals per 100 km², with adequate goodness-of-fit across multiple detection metrics. Despite low detection rates, the rt-SCR framework allowed for robust inference by maximizing data use and reducing bias associated with exclusion of unidentified detections. Our findings provide a baseline for future monitoring in northeastern Mexico and demonstrate the utility of rt-SCR models in data-limited contexts. These results support the implementation of localized conservation actions and long-term monitoring programs in peripheral jaguar habitats, where population viability may depend on maintaining ecological continuity and minimizing anthropogenic pressures.