Machine learning techniques often require large labeled training sets to attain optimal performance. However, acquiring labeled data can pose challenges in practical scenarios. Pool-based active learning methods aim to select the most relevant data points for training from a pool of unlabeled data. Nonetheless, these methods heavily rely on the initial labeled dataset, often chosen randomly. In our study, we introduce a novel approach specifically tailored for multi-class classification tasks, utilizing Proper Topological Regions (PTR) derived from topological data analysis (TDA) to efficiently identify the initial set of points for labeling. Through experiments on various benchmark datasets, we demonstrate the efficacy of our method and its competitive performance compared to traditional approaches, as measured by average balanced classification accuracy.