Janus particles, characterized by their anisotropic structure, exhibit rotational Brownian motion that causes periodic intensity fluctuations (blinking) when suspended in viscous fluids. This phenomenon provides a powerful optical signature for viscosity quantification. In this study, we developed a novel deep learning-based framework to analyze these blinking dynamics for rapid micro-volume viscosity estimation. We first performed extensive simulations of 1-μm Janus particles in fluids with viscosities ranging from 0.1 to 5.0 cP, systematically testing challenging conditions including high translational diffusion, particle aggregation, slow/fast blinking, high noise, and clinical scenarios. Experimental validation was conducted using real glycerol-water mixtures with viscosities ranging between 1.0 and 5.0 cP. A Python-based pipeline combining YOLOv8 instance segmentation and Trackpy tracking accurately detected and tracked individual particles across 140-frame video sequences recorded at 20 frames per second. The Continuous Wavelet Transform (CWT) was then applied to the intensity time-series of each tracked particle to generate time-frequency scalograms (0.05–20 Hz), from which flickering frequencies driven by rotational diffusion were extracted. Finally, a Convolutional Neural Network model, trained on a dataset of 480 CWT-generated intensity heatmaps (20 per viscosity level across 24 viscosity classes), achieved 95.8% accuracy with a mean error of only 0.33% and a 5-fold cross-validation accuracy of 92.7%. Single-blind tests on unseen experimental data yielded viscosity predictions with errors less than 2% with a computational time of less than 2 minutes, with aggregated and non-blinking particles effectively filtered in advance to ensure data quality. This work demonstrates the first seamless integration of instance segmentation, particle tracking, wavelet analysis, and deep learning for automated viscosity prediction, offering a rapid, non-invasive, and robust method with strong potential for applications in microfluidics, biosensing, and clinical diagnostics.