In the age of smart cities, where interconnected technologies produce substantial data, time series forecasting has become a crucial instrument for efficient urban management. Exploratory Data Analysis (EDA) is an essential initial phase in comprehending and organising data for precise and insightful predictions. This study examines the utilisation of exploratory data analysis approaches for time series data in the framework of smart cities. It emphasises techniques for detecting trends, seasonality, autocorrelations, and correlations in data streams from sources like environmental monitoring systems. The research investigates ways to identify patterns, test hypotheses, and confirm assumptions using visual and quantitative methods. Employing diverse visualisation tools, statistical summaries, and decomposition techniques enhances the comprehension and preprocessing of time series datasets. The objective of the research is to perform and evaluate the efficacy of standard tools such as time plots, autocorrelation and correlation analysis, seasonality decomposition, and identifying trend patterns in conjunction with sophisticated techniques such as seasonal decomposition of time series (STL), correlation heatmaps, and trend detection techniques. The findings emphasise EDA’s significance as a fundamental step in empowering researchers and practitioners to make informed decisions, ensuring strong analytical results, and developing resilient time-series forecasting models for contemporary urban issues.