The accurate prediction of drug responses based on the genomic profile of a patient is essential to progress in the field of precision medicine. The advent of various deep-learning algorithms based on publicly available large-scale omics datasets is the driving force behind research in this field. The characteristics of biological datasets, characterized by high dimensions and low sample sizes, pose challenges of overfitting and limited generalization in prediction models. Additionally, constructing prediction models using biological data such as gene expression is further complicated by the need to account for the complex relationships among genes, which exacerbates the aforementioned challenges. To address these challenges, we propose a drug response prediction framework (DrDiff) that integrates a denoising diffusion probabilistic model (DDPM) based data augmentation module with a graph attention network based drug response prediction module. The proposed model showed a 10% higher AUC than the state-of-the-art models for drug response prediction for the six drugs considered in the study, suggesting the superior generalization performance of DrDiff over other baseline models. Furthermore, we demonstrated the feasibility of generative models, which form one of the modules of the proposed framework, in overcoming the fundamental limitations of omics datasets. Further experiments bear out the feasibility of generative models, which form one of the modules of the proposed framework, in augmenting gene expression data.