Multichannel time series classification (MCTSC) has emerged as a critical research area in machine learning due to its wide applicability in healthcare monitoring, human activity recognition, and industrial signal analysis. Despite significant advances in deep learning and hybrid architectures, the interpretability of classification outcomes and the transparency of model evaluation remain major challenges. This paper proposes a conceptual and analytical visual analytics framework designed to enhance interpretability in machine learning–based multichannel time series classification systems. The framework integrates predictive modeling, feature representation learning, and visual performance diagnostics to enable comprehensive understanding of both model behavior and data dynamics. Building upon benchmark datasets and established methodologies in time series classification research (Bagnall et al., 2017; Bagnall et al., 2018), the study systematically evaluates how visual analytics can bridge the gap between model accuracy and interpretability. The framework also incorporates insights from spectral classification approaches and machine learning pipelines used in multivariate signal environments (Acuna et al., 2024). Experimental and comparative analysis suggests that combining visual interpretability layers with classification models improves diagnostic transparency, facilitates error analysis, and enhances decision confidence in real-world applications. The findings highlight the importance of integrating visualization-driven interpretability into next-generation time series classification systems.