The increasing global demand for food necessitates the optimization of agricultural practices, making crop yield prediction a crucial aspect of modern farming. This survey explores the diverse landscape of crop yield prediction models, encompassing various techniques, methodologies, and challenges encountered in this interdisciplinary field. The survey reviews traditional methods, such as statistical and regression-based approaches, alongside contemporary machine learning and data-driven models, including artificial neural networks, support vector machines, and ensemble methods. The survey delves into the key factors influencing crop yield, such as climate conditions, soil characteristics, and pest infestations, and examines how these variables are integrated into predictive models. This survey paper reviews 43 research papers for crop yield prediction and explores the potential of computer-assisted methods for crop yield prediction. Furthermore, the review addresses the importance of remote sensing technologies, satellite imagery, and IoT devices in capturing real-time data for enhancing prediction accuracy. Challenges and limitations in existing models are scrutinized, ranging from data scarcity and quality issues to the interpretability of complex machine learning algorithms. Moreover, the survey discusses the ethical considerations surrounding data privacy and ownership, as well as the potential socio-economic impacts of widespread adoption of predictive technologies in agriculture.