Handwritten character recognition holds a significant position in the realms of pattern recognition and machine learning, finding applications across archival document digitization and automated form processing. Convolutional Neural Networks (CNNs) have emerged as a powerful tool for addressing this task due to their ability to learn intricate features and patterns from raw input data. This paper provides a comprehensive overview of CNNs' application in handwritten character recognition, covering various aspects of the recognition process including preprocessing techniques, dataset selection, network architecture design, training methods, and performance evaluation. The synthesis explores the evolution of CNN-based methodologies, examining architectural enhancements, regularization techniques, and data augmentation methods that collectively improve recognition accuracy. Additionally, the paper evaluates advanced approaches that integrate CNNs with other machine learning techniques such as recurrent networks and attention mechanisms, leading to further improvements in recognition performance. Through a thorough review of contemporary literature, the paper highlights achievements, ongoing challenges, and emerging research directions in the field of handwritten character recognition using CNNs. This survey serves as a valuable resource for researchers and practitioners interested in understanding the current landscape of the field and sheds light on potential breakthroughs and the trajectory of advancements in this dynamic domain.