Air quality shows the degree to which the air is clean and suitable for living creatures and the environment. Recently, developing countries like India have been experiencing massive air pollution. The main cause of this poor level of air is the presence of dust particles, especially particulate matter (PM) with a 2.5-micrometer size. PM 2.5 mainly comes from road dust, fuel burning, vehicles, and industrial plants. Constant breathing of this polluted air causes many health problems. Therefore, we need accurate and real-time monitoring systems for checking dust particle concentration levels periodically. The data that is generated on a periodic or time basis is called time series data, and these data are unstructured. Time series data, or unstructured data analysis is an important area in artificial intelligence. Different deep learning and machine learning methods are used for handling this data. The time series data processing includes missing data imputation, hyper-parameter optimization, normalization, etc. This paper mainly focused on various methods and algorithms that are used in time series data processing and the metrics used to evaluate its performance. This will help to develop new models for handling time series data and thus predicting air quality efficiently. From the analysis of existing methods, inferred that deep learning models like Long Short-Term Memory (LSTM) and Multi-Layer Perceptron (MLP) give better accuracy. Air quality prediction with Geospatial Artificial Intelligence (GeoAI) is a new and emerging area for future research.