This paper offers a comprehensive overview of recent advancements in artificial intelligence (AI) algorithms and architectures employed in yield estimation within the agricultural field. The study begins by conducting a thorough science mapping analysis to assess the landscape of yield estimation research, examining trends in published literature to identify pertinent and popular topics. Subsequently, a curated list of popular datasets is presented, accompanied by an elucidation of the lifecycle of utilizing AI for yield