Growth rates vary throughout an animal’s life, making linear models inadequate for capturing major growth phases. Instead, robust non-linear models should be used. This study analyzed growth data from 400 progenies of four turkey genotypes: Nicholas White Turkey (NWT) × Locally Adapted Turkey (LAT), LAT × NWT, NWT × NWT, and LAT × LAT over 20 weeks. Data analysis was conducted using SAS (REML with fixed effects) and WinBUGS for the Markov Chain Monte Carlo (MCMC) process. Model selection was based on Deviance Information Criteria (DIC), Bayesian Information Criteria (BIC), and Akaike Information Criteria (AIC). Results showed that NWT exhibited superior growth (7465 g at 20 weeks). Across genotypes and sexes, an inverse relationship was observed between asymptotic weight (A) and maturation rate (K), with the strongest negative correlation in female exotic turkeys (− 0.866, − 0.985, − 0.967) for Logistic, Gompertz, and von Bertalanffy models, respectively. The von Bertalanffy model, along with REML and Bayesian approaches (normal and student t-distributions), best predicted turkey growth parameters A, B, and K. The Gompertz model produced estimates close to von Bertalanffy, making both suitable for turkey growth modeling. Additionally, Gompertz was the best fit for female exotic and female crossbred turkeys. The study concludes that non-linear growth model estimates should be used for growth comparison and selection in Nigerian Locally Adapted turkeys, with von Bertalanffy providing the best fit.