Temperature is a crucial parameter to consider due to its impact on daily activities. Additionally, it is influenced by other factors such as humidity, dew point, pressure, sunshine duration, and wind speed in the surrounding area. Data was obtained from Iran Meteorological Organization (IRIMO), from 2015-2021. This study evaluates models for predicting the next day\\\'s temperature every hour using Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and 1D Convolutional Neural Networks (CNN1D) across four scenarios: data from one day, two days, three days, and one week prior. Before being predicted, pre-processing is needed to improve data quality consisting of interpolation, sequence formation, encoding time information, and normalization. The results of this study prove that using the GRU model produces the best testing MSE, R^2, and SMAPE values of 3.27, 96.78%, and 27.16% for test data.

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