Quantitative real-time polymerase chain reaction (RT-qPCR) using a stable reference gene is widely used for gene expression research. Barnyard millet (Echinochloa spp.) is an ancient crop in Asia and Africa that is widely cultivated for food and fodder. It thrives well under drought, salinity, cold, and heat environmental conditions, besides adapting to any soil type. To date, there are no gene expression studies performed to identify the potential candidate gene responsible for stress response in barnyard millet, due to lack of reference gene. Here, 10 candidate reference genes, Actin (ACT), α-tubulin (α-TUB), β-tubulin (β-TUB), RNA pol II (RP II), elongation factor-1 alpha (EF-1α), adenine phosphoribosyltransferase (APRT), TATA-binding protein-like factor (TLF), ubiquitin-conjugating enzyme 2 (UBC2), ubiquitin-conjugating enzyme E2L5 (UBC5) and glyceraldehyde-3-phosphate dehydrogenase (GAPDH), were selected from mRNA sequences of E. crus-galli and E. colona var frumentacea. Five statistical algorithms (geNorm, NormFinder, BestKeeper, ΔCt, and RefFinder) were applied to determine the expression stabilities of these genes in barnyard millet grown under four different abiotic stress (drought, salinity, cold and heat) exposed at different time points. The UBC5 and ɑ-TUB in drought, GAPDH in salinity, GAPDH and APRT in cold, and EF-1α and RP II in heat were the most stable reference genes, whereas ß-TUB was the least stable irrespective of stress conditions applied. Further Vn/Vn + 1 analysis revealed two reference genes were sufficient to normalize gene expression across all sample sets. The suitability of identified reference genes was validated with Cu-ZnSOD (SOD1) in the plants exposed to different abiotic stress conditions. The results revealed that the relative quantification of the SOD1 gene varied according to reference genes and the number of reference genes used, thus highlighting the importance of the choice of a reference gene in such experiments. This study provides a foundational framework for standardizing RT-qPCR analyses, enabling accurate gene expression profiling in barnyard millet.