<p>The behaviour of heavy metal contamination is dynamic, influenced by seasonal changes, hydraulic fluctuations, and physical, chemical, and biological processes. It’s necessary, therefore, to extend heavy metal analysis to methods and models that describe their distribution and transportation. Thus, this systematic review has investigated the methodologies and models used to describe the distribution and mobility of contaminants in areas where water and soil media play a significant role. The review adhered to the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement as a guideline. An extensive search strategy was developed to ensure that relevant literature on heavy metal modelling in distribution and transportation was obtained. It is identified that the spatial distribution models can be grouped into machine learning, Bayesian and regression models, and satellite images. They handle complex spatial relationships and integrate diverse data sets. Their drawback is that they don’t include processes related to heavy metal dynamics. To account for the mobility of heavy metals, advection–diffusion equation, bivariate linear mixed models and spatial-temporal kriging with trend play a significant role. The study suggests an increased focus on the understudied dumpsites or landfills in the distribution and transport of heavy metals. The application of machine learning needs to be extended to temporal predictions, rather than spatial-only cases, which are frequently used. The temporal predictions should be improved by including multi-season or multi-event data. Also, the development of 3-dimensional models and non-equilibrium kinetics should be encouraged to account for the complex flows.</p>