Context <p>Managing wild populations in rapidly changing, human-dominated landscapes requires models that accommodate complex interactions among climate, land use, disease, and evolution. Agent-based models (ABMs) are well suited to this task but are often difficult to parameterize, calibrate, and interpret at management-relevant scales.</p> Objectives <p>We discuss how artificial-intelligence (AI) techniques, including machine-learning regression, data-mining diagnostics, geospatial informatics, and large-language-model code aides, can streamline ABM parameter estimation and scenario testing, enhance extraction of decision-support metrics, and broaden the accessibility of ABMs for conservation planning.</p> Methods <p>We considered examples of AI use in ecology and evolution, including where AI was paired with ABMs, highlighting use cases such as calibration, rule discovery, data fusion, and code generation.</p> Results <p>We show how supervised machine learning can supplement parameterization by learning relationships between empirical observations and model outputs. Data-mining methods may also be useful to identify parameters that drive most output variance. In addition, deep-learning remote-sensing products in ABMs allows landscape dynamics to be represented at ecologically relevant resolutions. Despite this, key obstacles, such as limited long-term ecological data, high computational demand, and the need for explainable-AI safeguards against biased predictions remain.</p> Conclusions <p>Expanding the use of AI in ABMs will require interdisciplinary collaborations that pair ecologists with computer and geo-information scientists and explicit workflows for auditing AI decisions. However, leveraging AI enhanced ABMs will improve predictive modeling of species responses to environmental change, optimize conservation strategies, and develop more effective data-driven management.</p>