In the case of temperature sensitive products (like food and pharmaceutical), cold chain logistics plays a pivotal role in product safety and effectiveness throughout shipping. Breakages Temperature excursions, or broken chains, are very dangerous and both can result in economic losses in addition to possible health risks to the population, in the case of vaccines and biologics. Conventional cold chain monitoring tools are reactive, which warns the stakeholders after a temperature violation has taken place. The paper introduces a fresh solution based on the shift between reactive and proactive cold chain monitoring through predicting the risks of the broken chain based on multi-modal data fusion and machine learning. The system incorporates Internet of Things (IoT) sensor and GPS data, refrigerated unit status, weather data and traffic information. The sophisticated time-series prediction systems, such as Long Short-Term Memory (LSTM) and Transformer networks, are used to predict the future of temperature changes within the upcoming 1-3 hours to allow proactive response to a temperature violation occurrence. Anomaly detection algorithms declare patterns that represent any high-risk situation, e.g., vehicles stagnating in traffic in severe weather conditions and compute a broken chain risk score of each shipment. In case, the risk score goes beyond a preset limit, the system sends alerts to both the drivers and the control centres. With the help of this predictive model, the cold chain can be a lot more reliable as it can be used to timely implement countermeasures, like rerouting or equipment maintenance, to lower levels of spoilage and increase safety. The presented strategy can be considered one of the paradigms of cold chain logistics because it can prove the effectiveness of machine learning in enhancing proactive risk monitoring and keeping temperature-sensitive products safe.