Photonic Crystal Fiber (PCF) has demonstrated a prospective use in several areas of contemporary optics because of its superior transmission properties. But when it comes to building PCF architectures and describing pulse propagation in PCFs, traditional numerical solution techniques have drawbacks including high computing effort along with low efficiency. With the development of Artificial Intelligence (AI) technology, several intelligent computing approaches particularly machine learning (ML) algorithms and deep learning (DL) algorithms offer novel approaches to address the challenges. Numerous exciting applications have resulted from the design of multi-analyte sensor. With the growing need for an intelligent world, intelligent photonics has advanced quickly in recent years. The intelligent evolution of optical fiber sensors has been significantly aided by AI. Through the introduction of creative ways to problem-solving, its influence goes beyond improving sensor performance. This chapter’s objective is to explain the properties that are acquired by integrating AI in multi-analyte sensing.