Building a system to forecast Covid-19 infected cases is of great importance at the present time, so in this article, we present two systems to forecast cumulative Covid-19 infected cases. The first system (DLM-System) is based on deep learning models, which include both long short-term memory (LSTM), bidirectional long short-term memory (Bi-LSTM), and Gated recurrent unit (GRU). The second system is a (TS-System) based on time series models and neural networks, with a Prioritizer for models and weights for time series models acting as an ensemble between them. We did a comparison between them in order to choose the best system to forecast cumulative Covid-19 infected cases, using the example of 7 countries. As some of them have finished the second wave and others have finished the third wave of infections (Russia, the United States of America, France, Poland, Turkey, Italy, and Spain). The criterion for choosing the best model is MAPE. It is a percentage, not an absolute value. It was concluded that an ensemble method gave the smallest errors compared to the errors of the models in the (TS-System).