Role of ozone in regulating climate and air quality, emphasizes the need for real-time monitoring of ozone levels. Ground layer ozone pollution is a major threat to the environment and human life. In this chapter, the importance of ozone analysis is presented to understand the dynamics of the atmosphere and reduce its damaging effects. Earlier, traditional methods like ground-based observations and satellite-based measurements were used. However, these methods required high installation costs and had low spatial resolution. These were the major limitations of traditional methods, which paved the way for Artificial Intelligence (AI) and Machine Learning (ML) models. Supervised machine learning models like regression, decision trees, and random forest models use historical data for ozone level predictions. Unsupervised models include clustering techniques to identify hidden patterns in larger datasets. ANFIS (Adaptive Neuro-Fuzzy Inference System) is a deep learning model that combines the principles of neural networks and fuzzy systems. Nowadays, ML techniques are also used for spatiotemporal analysis of ozone levels. Ozone analysis plays a crucial role in environmental policy making, as well as public health protection. This includes identification of ozone hotspots and areas at risk of exceeding regulatory thresholds. Besides, this chapter highlights challenges faced in ozone analysis like lack of monitoring infrastructure in some regions for leveraging AI and ML.