The automatic generation of radiology reports has emerged as an effective solution to address time-consuming clinical tasks and accurately identify key pathological features in X-ray images, attracting significant attention in recent years. Cross-modal mapping between images and text, a critical component for generating high-quality reports, presents considerable challenges due to the lack of corresponding annotations. In this study, we propose a Cross-modal Memory Alignment Framework with Disease Aware Contrastive Learning to generate coherent and informative reports by aligning visual and textual features. Specifically, we first embed both image and text features into a shared space through parametric query vectors, ensuring tight alignment between images and reports. The memory-aligned embeddings are then obtained by querying a memory matrix, where the query signals are derived from visual features. These embeddings subsequently guide the visual-text feature alignment during the report generation process. For model optimization, we integrate reinforcement learning with knowledge distillation techniques to achieve efficient knowledge transfer through iterative training. Extensive experiments on the IU X-ray and MIMIC-CXR benchmark datasets demonstrate that our method generates more accurate and clinically relevant reports, which further demonstrates the effectiveness of the proposed alignment method.