Emerging fiber optic sensing technologies have recently been utilized in a countless number of fields and applications due in large part to the ability of fiber optic sensing systems to detect various physical parameters such as strain, temperature, and pressure at high and continuous rates. Nonetheless, the data collected by fiber optic sensors provide enormous challenges in the processing and analysis of large datasets for real-time decision-making. Presently, using techniques of Machine Learning (ML), it is now possible to develop algorithms which can easily handle, analyze, and learn from the data collected through sensor. This chapter focuses on the possibility of merging the ML methods with fiber optic sensing systems, and the potential real-time analysis architectures applied to structural health monitoring, environmental crisis identification, medical diagnostics, and industrial process management. We also identify some of the key limitations including sensor noise, computational complexity and model size and limitations before moving on to the future trends include edge computing and IoT integration. The close supervision and regulation of a number of health, infrastructure, and other natural parameters have provided the needed impetus in the design and development of technological devices in various fields.