This chapter delves into the groundbreaking impact of deep learning (DL) on autism spectrum disorder (ASD) studies and therapies. Through various applications, such as neuroimaging analysis, genetic investigations, and behavioral evaluation, this chapter delves into how DL approaches transform ASD diagnosis, treatment, and understanding. Some important advancements in this field include sophisticated behavioral analytic systems that can evaluate social signals, facial expressions, and speech patterns and network-based techniques that may find genes connected to autism. Modern advances in explainable artificial intelligence (XAI) have improved the interpretability of models, and multimodal learning techniques have emerged to provide more thorough insights by combining various input kinds. This chapter recognizes the considerable promise of these technologies but also tackles important obstacles, such as data bias, privacy concerns, and the need for different datasets for training models. When discussing the use of artificial intelligence (AI) in healthcare contexts, this chapter stresses the need for careful and ethical application. To fully harness the potential of DL technologies, researchers, clinicians, and technologists must maintain close cooperation; however, their incorporation into autism research is a giant leap forward toward better personalized, accurate, and effective methods of ASD diagnosis and treatment.