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Time-series prediction is an important area that inspires numerous research disciplines for various applications, including air quality databases. Developing a robust and accurate model for time-series data becomes a challenging task, because it involves training different models and optimization. In this paper, we proposed and tested three machine learning techniques-recurrent neural networks (RNN), heuristic algorithm and ensemble learning-to develop a predictive model for estimating atmospheric particle number concentrations in the form of a time-series database. Here, the RNN included three variants-Long-Short Term Memory, Gated Recurrent Network, and Bi-directional Recurrent Neural Network-with various configurations. A Genetic Algorithm (GA) was then used to find the optimal time-lag in order to enhance the model's performance. The optimized models were used to construct a stacked ensemble …