Purpose: Narrative assessment is one potentially underutilized and inconsistent method speech-language pathologists may use when considering a diagnosis of developmental language disorder (DLD). However, narration research encompasses many varied methodologies. This systematic review and meta-analysis aimed to (1) investigate how various narrative assessment types (e.g., macrostructure, microstructure, and internal state language) differentiate children with typical development (TD) from children with DLD, (2) identify specific narrative assessment measures that result in greater group differences, and (3) evaluate participant and sample characteristics that may influence performance differences. Method: Electronic databases (PsycINFO, ERIC, and PubMed) and ASHAWire were searched on July 30, 2019, to locate studies that reported oral narrative language measures for both DLD and TD groups between ages 4 and 12 years; studies focusing on written narration or other developmental disorders only were excluded. We extracted data related to sample participants, narrative task(s) and assessment measures, and research design. Group differences were quantified using standardized mean differences. Analyses used mixed-effects meta-regression with robust variance estimation to account for effect size dependencies. Results: Searches identified 37 eligible studies published between 1987 and 2019, including 382 effect sizes. Overall meta-analysis showed that children with DLD had decreased narrative performance relative to TD peers, with an overall average effect of -0.82 SD, 95% confidence interval [-0.99, -0.66]. Effect sizes showed significant heterogeneity both between and within studies, even after accounting for effect size-, sample-, and study-level predictors. Across model specifications, grammatical accuracy (microstructure) and story grammar (macrostructure) yielded the most consistent evidence of TD-DLD group differences. Conclusions: Present findings suggest some narrative assessment measures yield significantly different performance between children with and without DLD. However, researchers need to improve consistency of inclusionary criteria, descriptions of sample characteristics, and reporting of correlations between measures to determine which assessment measures reliably distinguish between groups.