This systematic literature review delves into the intersection of deep learning applications within the aphasia domain, meticulously examining data from primary databases and employing advanced query methodologies. Synthesizing findings from 28 relevant documents, the study reveals a landscape marked by significant advancements and persistent challenges. Utilizing the PRISMA framework and Machine Learning-driven tools like VosViewer and Litmaps, the research elucidates the high variability in speech patterns, the complexities of speech recognition, and the hurdles posed by limited and diverse datasets as core obstacles. Initially, the study sourced 75 articles from databases including IEEE Explore, Scopus, Web of Science, and PubMed. Following the PRISMA framework, duplicates and irrelevant articles were excluded, resulting in 28 highly relevant articles. Beyond consolidating current knowledge, this review charts a course for future research, emphasizing the imperative for comprehensive datasets, model optimization, and integration into clinical workflows to enhance patient care. Ultimately, this work highlights the transformative potential of deep learning in advancing aphasia diagnosis, treatment, and support, signaling a new era of innovation and interdisciplinary collaboration in addressing this challenging disorder.