Feature selection allows reducing the dimensionality of data sets by selecting the most relevant features. This will lead to more interpretable models, in addition to reducing computational costs. In this context, we introduce a new nature-inspired feature selection technique based on a recently developed discrete optimization algorithm called the Binary Mother Tree Optimization (BMTO) algorithm. We call BMTO for Feature Selection (BMTO-FS) the new technique we propose. BMTO-FS utilizes a binary converter module that converts real numbers into binary numbers using a sigmoid function and a threshold value. BMO-FS operates within our new feature selection framework that can be generalized to any other metaheuristic. To assess the performance of BMTO-FS, we conducted extensive comparative experiments on several public benchmark datasets corresponding to low, medium, and high dimensions. In these experiments, genetic algorithms (GA), particle swarm optimization (PSO), and the whale optimization algorithm (WOA) are considered. The results are promising, as they show that BMTO-FS outperforms GAs, PSO, and WOA in terms of accuracy rates, number of reduced features, and computational cost.