With increasing dependency on satellite communications and navigation systems in air travel, telecommunications underscored the importance of effective monitoring and prediction of space weather phenomena. This paper reviews the implementation of machine learning methodologies in a learning-driven space weather dashboard that will report live data and make predictions and analytics in the aviation and telecommunication industries. This paper explores the role played by different indicators of space weather, including the solar X-ray flux, coronal mass ejection (CME), and solar wind measurements, emphasizing their significance for operational safety. Moreover, we evaluate literature on machine learning for space weather forecasting, highlighting their efficacy in improving predictive precision. The study concludes with a comparative review of the range of models and frameworks, feeding into future directions. and thus improving space weather monitoring. Space weather poses significant threats to industries like aviation and satellite communications, GPS systems, and high-frequency radio transmissions. The space weather event that could be predicted with accuracy, such as solar flares, geomagnetic storms, and coronal mass ejections (CMEs), is crucial for dealing with these risks. This research investigates the use of machine learning techniques for forecasting space weather and its possible effects on these sectors. Past space meteorological data used include the solar X-ray flux, solar wind velocity, and geomagnetic indices to design machine learning algorithms. The studied algorithms include Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Random Forest classifiers, which are tested in terms of their respective predictive validity.