The rapid evolution of electric, connected, autonomous, and shared (ECAS) vehicles is transforming automotive architectures and driving demand for complex, scalable, and efficient wiring systems, even as harness assembly remains a predominantly manual and ergonomically challenging process. However, reliable verification of electrical connector mating, traditionally performed by human operators via auditory and tactile feedback, remains challenging for full automation. This study introduces a multimodal sensing approach integrating acoustic, force, and kinematic data for robust, real-time detection of successful electrical connector mating. High-frequency acoustic signals capturing mechanical click signatures, combined with simultaneous force sensor data and robot end-effector motion profiles, provide complementary information to resolve ambiguous events. Various supervised learning algorithms, including convolutional neural networks (CNNs), multilayer perceptrons (MLPs), and random forest classifiers, are evaluated using a dataset including diverse connector types and ambient noise levels. Feature extraction techniques and dynamic thresholding mechanisms isolate critical signal features, enhancing performance under low signal-to-noise conditions. Designed for seamless robotic integration, the system delivers immediate feedback for downstream assembly processes. Achieving up to 96.8% accuracy with the deployed CNN, the approach demonstrates the viability of AI-driven multisensor fusion for reliable connector verification, facilitating agile, sustainable, and digitally integrated manufacturing systems.