Usage-Based Insurance (UBI) is revolutionizing how insurers calculate premiums based on observed driving habits, with telematics and connected vehicles providing growing potential for more responsive and fairer insurance. The traditional way of calculating the premium is based on the static models that curate the premium for an individual based on the past driving history, and neglecting the driving habits. This old method has both advantages and disadvantages, but it doesn’t provide a premium based on the risks of the drivers’ driving habits. Insureds were asked to pay the premium based on the algorithm, which focuses on the static rating tables rather than using the real-time user driving habits data. However, these system creates complex interactions between the insurer and insured, specifically for privacy, data manipulation, and self-interested driving behavior. This article highlights the role of artificial intelligence (AI) in enhancing Universal Basic Income (UBI) by analyzing data, refining risk modeling, and enabling dynamic pricing in real-time. Additionally, we model these interactions using dynamic game theory under incomplete information. For this, we define an insurer as a leader who sets pricing schemes and monitors strategies, and an insured as the follower who reacts to the incentives and possibly changes behavior. We propose a ready-for-action AI platform with individualized driver feedback, fraud detection, and dynamic pricing mechanisms, and derive equilibrium strategies for both insured and insurer, and propose a robust pricing method for strategic manipulation. The simulation-based synthetic driving data highlights how game-theoretic pricing can perform better than traditional pricing methods in all aspects. The study also elaborates on key regulatory and moral implications and charts the way forward with future evolution and research gaps in this new area of driving, where technology drives the future.