Recent advances in machine learning have inspired the development of deep neural network (DNN)-based smart sensing applications for the Artificial Internet of Things (AIoT). However, the effectiveness of DNNs relies on the availability of large, labeled data to uncover useful feature representations. The widespread use of DNN models in computer vision (CV), natural language processing (NLP), and voice sensing can be attributed to the massively available labeled training datasets. Despite the abundance of IoT sensing data, the human-uninterpretable property of AIoT data makes it difficult to construct labeled datasets for DNN model training. Additionally, variations in sensor hardware or DNN models’ deployment environments introduce domain shifts, making generalized machine learning algorithms even more difficult to develop. The scarcity of labeled training data and run-time domain shifts are two main challenges in developing effective machine learning algorithms for AIoT sensing. The goal of my research is to address the above challenges for AIoT sensing applications. Two main research methodologies are involved. The first is to leverage the latest state-of-the-art machine learning techniques to develop effective models for smart sensing. The second approach involves integrating known prior knowledge into machine learning algorithms to develop more accurate and reliable DNN models for AIoT sensing applications.