To address asynchronous acquisition, heterogeneous data dimensions, and the difficulty of directly applying multi-source measurements to fusion modeling in online water quality monitoring, this paper presents a multimodal water quality detection system for data fusion. The system uses a 940 nm light-emitting diode (LED) near-infrared scattering channel for turbidity and particulate-matter characterization, and integrates 200–1000 nm broadband spectral detection, pH detection, and electrical conductivity detection. An embedded node performs data buffering and preliminary processing, while PC-based software manages calibration, spectral preprocessing, timestamp recording, and nearest-neighbor temporal matching. The resulting multimodal data frames combine scalar electrochemical and scattering features with high-dimensional spectral features under explicit temporal correspondence. Experimental results show that the pH, electrical conductivity, and near-infrared scattering turbidity modules provide calibratable and repeatable scalar outputs, while the spectral module outputs transmittance and absorbance features. In an asynchronous alignment experiment using the electrical conductivity stream as the reference, the system matched 77 out of 77 frames, achieving a 100% alignment success rate and a mean maximum temporal deviation of 0.437 s. A 72-sample preliminary fusion verification further indicates that adding pH and suspended-solids-related auxiliary information to spectral features reduces the mean five-fold cross-validation relative error for all tested indicators. These results demonstrate that the proposed system provides a practical data acquisition foundation for optoelectronic water quality detection, spectral compensation, and multimodal data fusion.