Brain networks have been quickly growing as of late, with novel procedures and applications. Nonetheless, difficulties like interpretability, reasonableness, heartiness, security, trust, and reasonableness stay strange in brain network advancements, notwithstanding the way that they will undeniably be tended to for basic applications. Endeavors have been made to conquer the difficulties in brain network registering by addressing and implanting area information with regards to emblematic portrayals. Thus,the neuro-emblematic learning (NeSyL) thought arose, which integrates parts of emblematic portrayal and bringing good judgment into brain organizations (NeSyL).The existing of Neurosymbolic Support Learning (Neurosymbolic RL). Contrasted with customary learning strategies, Neurosymbolic simulated intelligence offers huge by very intricacy giving straightforwardness and unexplainability. Support Learning(RL), a short-standing Fake Intelligence(AI) idea that mirrors human conduct utilizing prizes and discipline, is a key part of Neurosymbolic RL, a new reconciliation of the two fields that has yielded ominous outcomes. In spaces where interpretability, thinking, and reasonableness are urgent, for example, video and picture subtitling, question-responding to and thinking, wellbeing informatics, and genomics, NeSyL has shown promising results. This survey presents a complete overview on the best in class NeSyL approaches, their standards, propels in machine and profound learning calculations, applications, for example, opthalmology, and in particular, future points of view of this arising field.