Optical fiber sensor’s utilization is gaining momentum in different fields such as telecommunications, medical diagnostics, and structural health monitoring due to their high sensitivity, wide bandwidth, and immunity to electromagnetic interference. The precision and design of these sensors can be further improved with the help of cutting-edge deep learning models which has the capability to significantly amplify the capabilities of the sensors. This chapter delves into the capability of deep learning algorithms, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), long short-term memory (LSTM), and gated recurrent unit (GRU) enhance the performance and functionality of optical fiber sensors. The focus of this chapter is implementation of deep learning for signal processing, pattern recognition, and anomaly detection in fiber optic sensors. The chapter also covers advancements in data fusion techniques, where deep learning combines information from multiple sensors to provide more comprehensive insights compared to conventional techniques. This extensive comparative analysis would lead to more efficient utilization of deep learning techniques for future advancements, ensuring that this technology continues to evolve and meet the growing demands of various industries.