The identifiability results presented in this paper have significant practical implications for various fields where interacting particle systems are modeled on Riemannian manifolds. For instance, in physics, understanding the interaction kernels can lead to more accurate simulations of particle dynamics, which is crucial for predicting behaviors in systems ranging from atomic interactions to astrophysical phenomena. In ecology, these results can enhance models of animal flocking or predator-prey dynamics, allowing for better conservation strategies and understanding of ecosystem behaviors. In social sciences, the identifiability of interaction functions can improve models of opinion dynamics, enabling researchers to analyze how individual behaviors influence collective outcomes. The ability to recover interaction kernels from observational data, even in the presence of noise, ensures that these models can be effectively calibrated and validated against real-world data, thus bridging the gap between theoretical models and empirical observations.