In the era of big data, the sheer volume of information available for machine learning applications has grown exponentially. However, this increase in data often leads to a decrease in the quality of datasets due to issues such as noise, redundancy, and irrelevance, which can adversely affect the performance of predictive models. To address these challenges, dimensionality reduction techniques are employed, with feature selection being a prominent method. The objective of this research is to enhance machine learning prediction performance by developing a novel hybrid feature selection algorithm. This algorithm synergizes the strengths of cat swarm optimization (CSO) with those of the crow search algorithm (CSA), aiming to refine feature selection processes. The proposed hybrid algorithm was meticulously implemented and applied using the K-Nearest Neighbors (KNN) model on a diverse array of datasets. To rigorously evaluate its efficacy, the algorithm was tested on 12 carefully selected datasets, encompassing various domains and complexities. The performance was measured based on the accuracy of the machine learning predictions. Remarkably, the proposed hybrid algorithm achieved an average accuracy rate of 87%, which represents a significant improvement over previous approaches that had an average accuracy of 83%. These results underscore the potential of the proposed hybrid feature selection algorithm in enhancing the predictive capabilities of machine learning models by effectively reducing dimensionality and eliminating irrelevant features. The findings suggest that integrating CSO and CSA can lead to more robust feature selection mechanisms, thereby improving the overall quality and reliability of machine learning predictions.