<p>Kidney transplantation is usually the optimal treatment option for patients living with kidney failure given its associations with improved survival, quality of life outcomes and a reduction in the personal, economic, and societal burden of long-term dialysis. While advantages of kidney transplantation are recognized, post-transplant complications, such as graft rejection, ischemia–reperfusion injury, surgical-related complications, and long-term consequences of immunosuppressive therapies, are commonly observed. There has been increased research on developing non-invasive biomarkers for the monitoring of transplanted kidneys over recent decades. The potential of urinary biomarkers to identify graft rejection, post-transplant acute tubular necrosis, detect progression of epithelial-to-mesenchymal transition toward tubulointerstitial fibrosis, and to differentiate between causes of graft dysfunction is an attractive alternative to invasive transplant biopsy. Innovative urinary biomarkers, such as those derived from omics technologies allow for a more holistic assessment of graft status through multi-parametric molecular analysis, although there remain questions on the consistency, reliability, and practicality of utilizing omics-based urinary biomarkers. The international nephrology community has continued to make concerted efforts to improve the procedures and cost-effectiveness of kidney transplant monitoring. In this article, we review the evidence and limitations of currently available urinary biomarkers and propose the application of urine-derived exfoliated kidney cells such as urinary exfoliated proximal tubule cells to prognosticate kidney transplant outcomes and monitor for post-transplant complications. Artificial intelligence and the incorporation of machine learning analysis of proximal tubular cell characteristics may optimize the process of differentiating graft rejection from other forms of kidney dysfunction non-invasively following kidney transplantation.</p>