Statistical models for predicting potential laying pattern were important for economically optimal breeding strategy of egg production in a poultry flock. The aim of this study was to establish an optimal model for describing egg production using room temperature, feed consumption, layer weight, and age during the production period. The following mathematical models were used PSO-LSSVM (Particle swarm optimization-Least squares support vector machines) and PCA (Principal component analysis). The daily recorded of egg production data from 19,666 laying-type hens was used. Hen-daily egg production was described using egg-laying rate during successive days after reaching sexual maturity (120 days of age) and daily recorded room temperature, feed consumption, layer weight, and age. Then present study used PCA to study the correlation between this data. Using the Pearson correlation coefficient of the five factors (maximum and minimum shed temperatures, layer weight, feed consumption, and age) and egg-laying rate, the present study weighted each factor according to its influence on the egg-laying rate. In addition, LSSVM was used to create a regression model of the weighted data, and PSO was to optimize parameters (penalty coefficient c and kernel parameter g) in the LSSVM. Our experimental results showed that the goodness-of-fit criteria value (MSE) was small, lower than that of existing prediction models. The PSO-LSSVM model was able to fit well egg-laying rate of the whole Hy-Line Brown laying-type hens' flock.