<p>Understanding and quantifying the anthropogenic factors that influence the abundance of rapidly expanding species, such as the wild boar (WB; <i>Sus scrofa</i>), is critical for implementing effective regulation, targeted control measures, and sustainable population management. Based on hunting statistics, we assessed the potential of incorporating hunting-related variables alongside environmental factors to improve the predictive power of WB abundance models using generalised linear mixed models (GLMM). We used the maximum number of WB shots (MWBS) across 838 municipalities in five Moroccan regions between 2015 and 2019 as a proxy for the relative abundance of WB. Our results showed that incorporating hunting variables improved the predictive performance of the MWBS, reducing the mean absolute error by 2.9 and the root mean square error by 6.4 and increasing the marginal <i>R</i>² by absolute 34.8%. GLMM showed higher MWBS in anthropized municipalities, especially those with extensive agricultural (in three regions), built-up areas, and high population density (in two regions). High MWBS was recorded in municipalities with extensive holm oak cover and a high number of hunters. MWBS was positively associated with the extent of hunting society areas and negatively correlated with the proportion of open forest land. We recommend targeted management and monitoring strategies, alongside modelling improvements, to (i) enhance the predictive accuracy of WB relative abundance models by integrating environmental and hunting variables, (ii) support effective WB management in human-dominated landscapes, and (iii) deepen understanding of the combined influence of environmental and hunting variables in predicting species abundance across natural and urbanized landscapes.</p> Graphical Abstract <p></p>