Population development is becoming an increasingly studied area. The growing number of people also leads to greater consumption of natural or state resources. By detecting un\-expected trends early, it is possible to implement measures to prevent such situations. The recent development of machine learning has provided tools capable of identifying such trends. This work examined existing approaches used to predict population development, specifically the statistical methods ARIMA and ARIMAX with machine learning (XGBoost, LightGBM, Random Forest, RNN, GRU, LSTM). Based on a dataset containing information on more than 200 populations, two hybrid models combining ARIMA + XGBoost and LSTM + XGBoost were designed and implemented. These models can capture the trend of the selected population and predict the development of target parameters. In best cases, the models could predict gender distribution with an error of 0.005\% (based on RMSE metric) and development of age group distribution with an error of 0.1\% (based on RMSE metric). The ARIMA + XGBoost is effective for the short-term predictions, but the combination LSTM + XGBoost can generate longer predictions, although with less accuracy. The resulting models are used for predicting and adjusting demographic parameters in the creation of digital respondent populations, which is a focus area of the company Lakmoos AI, s.r.o.