Machine learning (ML) and deep learning (DL) technologies are advancing sensing capabilities by significantly improving accuracy, sensitivity, and adaptability. These advancements impact diverse sectors such as industrial automation, robotics, biomedical engineering, and civil infrastructure monitoring. The transformative potential of this shift is rooted in the integration of artificial intelligence (AI) with sensor technologies, emphasizing the development of efficient algorithms that optimize sensor performance and enable innovative applications across biomedical and engineering disciplines. Sensor calibration remains a fundamental process to ensure data accuracy and reliability across various sensing devices. Conventional calibration methods are typically time-intensive, involve manual intervention, and lack adaptability to dynamic environmental changes. AI-driven sensor calibration techniques have emerged to address these limitations by enhancing precision, minimizing human effort, and facilitating real-time calibration adjustments. This paper critically examines recent advances in AI-based sensor fusion and calibration techniques, highlighting key methodologies, associated challenges, and prospective research trajectories in this rapidly evolving field.