In this study, we present the development of an intelligent neural sensor to predict powder clogging during laser cladding by using machine learning techniques. A major factor contributing to these problems is the difficulty in accurately measuring powder mass flow rate, an essential open-loop variable that determines powder output from the nozzle. Accurate measurements of mass flow rate are essential for optimal process control and prediction of clogging events. To overcome these challenges, an experimental design was conducted manipulating factors such as laser power, travel speed, Z-step, N-layers, nozzle-to-substrate distance, and powder feed rate. The powder mass flow rate served as an independent variable to evaluate the neural sensor's predictive ability of clogging. A combination of three cameras and a microphone was used for real-time data acquisition during the experiments. The cameras captured the deposition process from different perspectives and generated thermal images, while the microphone recorded acoustic emissions. Customized cladding equipment enabled seamless data collection throughout the process. The results show that the smart neural sensor accurately monitors clogging during laser cladding, highlighting its importance to manufacturing. The developed models are able to effectively estimate the mass throughput under different parameters and temperature variations of the substrate material. Future studies may address the prediction of more complex coating properties such as porosity, crack formation, and metallurgical grain size distribution, thus expanding the potential of laser cladding and autonomous manufacturing systems.