Studies that have utilized macroinvertebrate communities as indicators of the ecological condition of streams and rivers in the Afrotropics use diverse methods, including diversity, biotic and multimetric indices. However, some of these indices are region- or country-specific, which limits their general use across multiple regions or countries. In this study, we address this challenge by testing and comparing the performance of regional biotic indices, diversity and richness indices (e.g., Shannon-Wiener and Simpson), and a macroinvertebrate-based index of biotic integrity (M-IBI) in assessing the ecological condition of the transboundary Mara River, Kenya and Tanzania. In this study, we analyzed water and habitat quality degradation caused by multiple stressors such as land use change, organic pollution and flow variation and the corresponding responses in macroinvertebrate communities. We utilized macroinvertebrates data collected from over 140 sites covering the entire gradient of the river and its major tributaries in Kenya and Tanzania. To develop the M-IBI, we used 12 metrics that describe macroinvertebrate community richness, composition, tolerance to disturbances, and functional feeding groups. Although all the biotic indices were sensitive to poor water quality and human disturbances of the river, the M-IBI responded to all forms of human disturbance (land use change, organic pollution and flow alteration) in the river. Thus, the M-IBI performed better than biotic and diversity and richness indices by having a higher discriminatory ability of site categories according to different types of disturbance. Diversity indices performed poorly and failed to discriminate between stressor gradients in the river. This study demonstrates a need for testing and evaluating indices developed elsewhere before adoption and used in biomonitoring streams and rivers in other countries and regions. There is even a greater need to evaluate the tolerance of macroinvertebrate taxa before inclusion in biotic indices for improved performance as discriminators of multiple stressors.