We introduce Memory Selection with Contrastive Learning (MSCL), an advanced Continual Learning (CL) approach, addressing challenges in dynamic and imbalanced environments. MSCL combines Feature-Distance Based Sample Selection (FDBS) for memory management, focusing on inter-class similarities and intra-class diversity, with a contrastive learning loss (IWL) for adaptive data representation. Our evaluations on datasets like MNIST, Cifar-100, mini-ImageNet, PACS, and DomainNet show that MSCL not only competes with but often surpasses existing memory-based CL methods, particularly in imbalanced scenarios, enhancing both balanced and imbalanced learning performance.