Scattering muon tomography leverages the multiple Coulomb scattering of cosmic-ray muons to image the internal structure of dense or shielded objects. Unlike transmission-based methods that rely on muon attenuation, scattering tomography measures angular deviations to infer the presence and composition of high-Z materials with high sensitivity. This presentation provides an overview of key imaging approaches used in scattering muon tomography, including point-of-closest-approach (PoCA), statistical reconstruction techniques like maximum likelihood and Bayesian inference, and recent developments in machine learning-assisted image reconstruction. We discuss the trade-offs in spatial resolution, detection efficiency, and computational complexity across these methods, with examples drawn from applications. Particular attention is given to how algorithmic choices and detector geometry influence imaging performance in real-world environments.