This research explores the application of advanced technologies to enhance the precision, accessibility, and effectiveness of weather forecasts, presenting benefits to individuals, businesses, and communities. The research utilises data collected from IoT-enabled weather stations, employing appropriate IoT protocols for data acquisition. This data undergoes pre-processing and normalisation to facilitate further analysis. The primary objective is to monitor and predict weather changes, specifically focusing on the probability of rainfall in London. This is achieved through a detailed examination of various meteorological parameters such as temperature, humidity, wind speed, dew point, and wind gusts. Through feature engineering, critical predictors are identified and optimised by eliminating redundant elements, thus refining the model's efficiency. Key features such as temperature, humidity, wind speed, gusts, air pressure, and dew point are analysed alongside temporal variables like time of day, day of the week, and seasonal patterns. The weather conditions are classified into three categories: Cloudy, Fair, and Rain. The dataset spans from 2014 to 2023, with a 70% split for training and 30% reserved for testing. Upon evaluating 17 distinct classifiers, the Support Vector Machine (SVM) classifier emerged as the most effective, demonstrating an 88% recall, 70% precision, and a 78% F1-score. These findings highlight the potential of integrating machine learning with real-time weather monitoring to predict weather patterns accurately.