In order to prevent future pandemics or epidemics, our project involved studying the evolution of Covid-19 cases and the impact of climate on its spread. We used machine learning models to analyze and better predict the spread of COVID-19 in Côte d’Ivoire, based on Covid-19 cases and climate data from 11 March 2020 to 28 January 2022 for the city of Abidjan. As the data is temporal, we used time series analysis models such as ARIMA, Facebook Prophet with additive and multiplicative seasonality and the LSTM recurrent neural network. The root mean square error (RMSE) was used to study the performance of the models. Firstly, the study of cases without climate data showed that the ARIMA (2,1,3) model performed best (RMSE cross-validation: 66.35; RMSE test: 132.69). Secondly, the study with data showed that climate has an impact on the spread of the disease with the LSTM model (RMSE cross-validation: 2.94; RMSE test: 14.63) performing much better. On the other hand, we noted drawbacks in the use of the Prophet and ARIMA models in view of the high discrepancies between the validation and test scores. This study also shows that climate change can have an impact on the spread of a virus or disease. This is shown by the cold temperatures during holiday periods and festivities, when people are increasingly close to each other. So, it’s important for the authorities and the medical community to take climatic factors into account when making decisions about epidemics.