Computing machines have traditionally been used to take a specific set of inputs and provide the system’s output employing a provided algorithm/function. However, with the rise of machine learning algorithms, it is now possible to provide a set of input and output values and let the machine discover the function that maps them together. Machine learning and deep learning are transforming the field of artificial intelligence and driving significant advancements across various domains in the past decade. Machine learning, a branch of artificial intelligence, allows systems to learn from data and make decisions without explicit programming. Deep learning, a subset of machine learning, leverages neural networks with multiple layers to model and interpret complex patterns in large datasets. The application of machine learning and deep learning models is increasingly vital for enhancing the overall performance of FSO systems. This chapter offers an overview of how these machine learning models could be utilized in future generation wireless communication systems. The chapter discusses the various existing contributions for the implementation of machine learning algorithms in the field of optical communication systems and then suggests the future research directions to use deep learning models for enhancement of FSO technology.