Gene selection plays a crucial role in the pre-processing of microarray data, aiming to identify a small set of genes that enhances classification accuracy and reduces costs. Traditional methods, such as Genetic Algorithms (GA) and Maximum Relevance Minimum Redundancy (MRMR), have been widely used, but bio-inspired algorithms like Ant Colony Optimization (ACO) and Ant Lion Optimizer (ALO) have shown promising results. These algorithms are based on natural processes: ACO mimics the foraging behavior of ants, while ALO models the hunting strategy of ant-lion larvae. However, both approaches face challenges like premature convergence and inefficient feature space mapping when used individually. To address these issues, this work introduces a hybrid ACO-ALO method, combining the strengths of both algorithms. The proposed hybrid approach enhances feature selection by improving accuracy, reducing computational complexity, and boosting classifier performance. The proposed model, which identifies the optimal feature set for classification using Support Vector Machine (SVM), has achieved an impressive prediction accuracy of 93.94 %. Results on microarray datasets for leukemia prediction show that the hybrid approach outperforms other methods in terms of both effectiveness and efficiency. This work demonstrates the potential of hybrid optimization techniques in bioinformatics for better gene selection and cancer diagnosis. • Hybrid ACO-ALO approach combines strengths of both algorithms for better feature selection. • Enhances classifier performance while reducing computational complexity. • Outperforms traditional methods on leukemia prediction datasets. © 2025 Elsevier B.V., All rights reserved.