The study aims to provide a new frailty model for modelling unobserved heterogeneity in survival data. We proposed a Weighted-Xgamma (Wxg) distribution as frailty to investigate the statistical characteristics of the distribution and Laplace transform function that may be used to calculate hazard and marginal survival functions. To fit the models, Weighted-Xgamma distribution as frailty and parametric distributions such as Exponential, Weibull, Log-Logistic, and Lognormal as baseline distribution were used. The Expectation-Maximization (EM) algorithm is suggested to estimate the parameter of the models. The Akaike information criterion (AIC) and the Bayesian information criterion (BIC) were used to assess the model fitness. To fit the proposed model, a well-known veterans Administration lung cancer study data set was applied. The study results revealed that the Weighted-Xgamma (Wxg) frailty distribution shows a better fit than the other frailty models. So we suggested the WeightedXgamma (Wxg) frailty model is an alternative approach for survival analysis.