The rapid growth of environmental data has significantly increased the need for efficient analytical frameworks capable of transforming raw meteorological information into meaningful insights. Weather data, which includes parameters such as temperature, humidity, rainfall, and wind speed, is inherently complex, high-dimensional, and continuously evolving. Traditional weather monitoring systems primarily focus on data collection and basic reporting, often lacking advanced analytical and visualization capabilities required for comprehensive understanding. This research presents a data science-driven approach for analysing and visualizing weather data through an interactive dashboard system. The proposed study emphasizes the application of data preprocessing, statistical analysis, and visualization techniques to extract meaningful patterns and trends from historical and real-time weather datasets. By leveraging modern data science tools, the system enables users to interpret climatic variations effectively and supports informed decision-making. The research further investigates the role of visualization in simplifying complex datasets and enhancing user comprehension. Graphical representations such as line plots, bar charts, and heatmaps are utilized to highlight temporal and spatial variations in weather parameters. The findings suggest that integrating analytical techniques with interactive visualization significantly improves the accessibility and usability of weather data. Despite its advantages, the study acknowledges certain limitations, including data inconsistency, dependency on external APIs, and scalability concerns. However, the proposed framework demonstrates strong potential for future enhancement through the integration of predictive models and real-time analytics. Overall, this research highlights the importance of data science in advancing weather data analysis and promoting data-driven environmental awareness.