Single-cell RNA sequencing (scRNA-seq) is a valuable tool for investigating cellular dynamics at the transcriptome level, enabling researchers to delve into the interactions of cells within the tumour microenvironment as well as to assess the impact of drug response on individual cells. The rapid advancement of scRNA-seq technology has given rise to more comprehensive profiles of transcriptomic heterogeneity within tumour subpopulations compared to traditional bulk sequencing analyses. These advancements in scRNA-seq have significantly expedited the discovery of disease biomarkers and the identification of therapeutic targets. While different approaches have been proposed for predicting drug responses through gene expression analysis in scRNA-seq data, there is a need for an integrated tool that encompasses both scRNA-seq analysis and drug repositioning. Computational drug repositioning, which involves freely available databases and AI-based tools and models, has successfully delineated differential signatures from complex heterogeneous data to predict potential anticancer drugs. Here, we comprehensively review the recent development and utilisation of artificial intelligence (AI)-based tools, specifically machine learning (ML) and deep learning (DL), for drug repositioning studies in cancer. We believe that the integration of AI along with single-cell technologies will ultimately transform drug development and targeted therapies, leading to improvements in precision, efficacy, and personalised cancer treatment.