Abstract: Excess domestic waste generation during religious festivals has adverse environmental impacts. The holy month of Ramadan provides opportunity to study relationship between food consumption pattern and waste generation. Therefore, the objective of this research is to investigate the relationship between Ramadan fasting and the generation of domestic solid waste in Bahrain for the period between 2019-2022. Furthermore, this study develops accurate time-series models using deep machine learning approaches to predict domestic waste generation for pre-, during, and post-Ramadan periods. The research findings revealed that during the month of Ramadan, the Bahraini's generates the highest amount of waste, with an average of 1,570 tons per day, compared to 1,450 tons per day during the rest of the year. The study conducted a comparative analysis of three machine learning models: Long Short-Term Memory (LSTM) Recurrent Neural Networks, Bidirectional LSTM (BILSTM) Recurrent Neural Networks, and Gated Recurrent Unit (GRU), along with three statistical models: Autoregressive Integrated Moving Average (ARIMA), Seasonal Autoregressive Integrated Moving Average (SARIMA), and Prophet. The prediction of BILSTM model outperformed the others with MAE value of 28.120, RMSE value of 36.879, MAPE value of 0.020, and R2 value of 0.78.