<p>The performance of machine learning algorithms is significantly influenced by the quality of the underlying dataset, which often comprises a mix of essential and redundant features. Feature selection, which identifies and discards these redundant features, plays a pivotal role in reducing computational and storage overheads. Current methodologies for this task primarily span filter-based and wrapper-based techniques. While Ant Colony Optimization, a popular bio-inspired meta-heuristic technique, has been extensively used for feature selection, employing mutual information as a principal heuristic measure, traditional mutual information is primarily suited for categorical features. To address this limitation, this study introduces an Embedded-Filter Ant Colony Optimization feature selection strategy that incorporates Clustering-Based Mutual Information. This integration offers enhanced support for classification tasks involving continuous features. To validate the efficiency of the proposed approach, various datasets were used, and a diverse range of machine learning algorithms were employed to evaluate the derived feature subsets. In addition to comparing the proposed method with Grey Wolf Optimization and Cuckoo Search Optimization-based feature selection approaches, a comprehensive evaluation was also carried out against established Ant Colony Optimization wrapper techniques. Experimental results indicate that the proposed Embedded-Filter Ant Colony Optimization consistently selects the minimal yet most relevant feature set while largely maintaining the efficacy of machine learning algorithms.</p>