It has revolutionized the production of realistic data in various disciplines and had increasingly important applications in sensor data processing. This work explores the Generative Artificial Intelligence (AI) models applied to sensor data, including diffusion models, variational auto encoders, and Generative Adversarial Networks. Sensor data often contains noise, missing values, and non-stationary behaviors that degrade the capabilities of traditional processing methods. Sensor data is widely used in Internet of Things (IoT) applications, healthcare data analysis, and environmental monitoring. Generative AI models will overcome such problems by generating realistic, high-quality sensor data and thereby improve sensor fusion, anomaly detection, and data augmentation. Hence, these are crucial models for real-time monitoring and decision-making in sensor-based systems by creating new sensor values while also allowing better accuracy of data points and even predicting missing values. This chapter discusses applications, difficulties, and future research prospects while giving a broad overview of cutting-edge generative AI algorithms and how they’ve been tailored to deal with sensor data.