<p>Fused deposition modeling (FDM) is an additive manufacturing (AM) technology recognized for its ability to easily and efficiently prototype complex geometries. However, its sensitivity to factors, such as material properties, temperature, and printing speed can influence the quality and mechanical characteristics of the printed parts. This study aimed to advance real-time anomaly detection in FDM by leveraging multimodal time-series data from sensors. Our approach introduces and evaluates novel hybrid architectures combining clustering algorithms with classification machine learning algorithms to monitor FDM processes through acoustic and vibration data. By fusing multiple data sources, such as inertial measurement unit (IMU) with acoustic signals, both independently and in combination, we investigate clustering–classifier pairings, revealing that some combinations, such as support vector machine (SVM) with agglomerative clustering and random forest (RF) with Gaussian mixture model (GMM) labels, yield higher classification accuracies. This study extends beyond previous approaches by analyzing clustering efficacy within feature spaces of varying dimensionalities. The results indicate that models with moderate dimensions (e.g., 20 components) enhance classification accuracy and better capture anomaly nuances. Furthermore, the paper benchmarks the compatibility of clustering techniques (K-means, agglomerative, GMM) with classifiers like SVM, RF, and decision tree (DT), providing insights into optimal clustering–classifier combinations for 3D printing anomaly detection. The findings underscore that SVM and RF perform exceptionally well in multimodal datasets with hierarchical clustering, especially in cases requiring high classification precision. These insights contribute to advancing FDM process monitoring, laying the groundwork for robust multimodal anomaly detection systems capable of accurately assessing complex and dynamic manufacturing conditions.</p>