Regular management practices are crucial to assessing colonies’ conditions and implementing measures to improve their strength. However, constant revisions can induce stress and even contribute to swarm loss. There- fore, effective management that considers the well-being of the bees is necessary. In order to assist the bee- keeper in managing the hives, this study proposes a noninvasive approach integrating Apis mellifera L., 1758 (Hymenoptera: Apidae) colony sound processing with machine learning and deep learning techniques to identify colony strength, essential for the productivity of apiculture. We developed an audio acquisition process focused on colony strength, resulting in a dataset with 3702 samples. We explored features extracted by CNNs, including VGG16, ResNet50, MobileNet, and YOLO, comparing them with cepstral features such as Mel-Frequency cepstral coefficients (MFCCs). Cepstral features significantly outperformed those extracted by CNN, with MFCCs achieving an accuracy of 95.53%, compared to the 78.99% achieved by the best-performing CNN. These results highlight the effectiveness of MFCCs in accurately identifying hive strength. This work differs from literature because it presents a protocol for categorizing beehives as either weak or strong, with a focus on reducing intervention time. It also includes a public dataset containing MFCCs and Deep Features extracted from audio recorded at different apiaries. Additionally, it offers a method for automatically classifying hives based on their strength. These contri- butions aim to serve as a knowledge base for the scientific community and to support beekeepers in non-invasive and cost-effective apiary management.