The paper concentrates on the latest developments in the field of deep-learning-based image deblurring and specifically, Convolutional Neural Networks (CNNs) and how they are able to be used to deblur images. It discusses the different forms of blur such as motion blur, out-of-focus blur, and mixed blur and compares these methods under the basis of blind and non-blind methods. The article sheds light on the various architecture and model design, loss functions, and performance indicators applied in image deblurring. Moreover, it draws attention to the issues that are presently observed in the sphere and gives possible path directions of the future research. The review has condensed and synthesized existing literature to provide a clear overview of the current solutions in image deblurring and offers guidance to the researchers on how to come up with the more precise, efficient, and adaptive methods of deblurring. The developments are meant to enhance the use of image restoration techniques in practical applications and this will lead to the quality and reliability of deblurring processes.