Transformers are important components of electricity transmission and distribution systems. Failure rate of transformers in India is in the order of 12-15 percent as against less than 1 percent in the developed countries. Sudden failure of transformers causes huge revenue loss not only to utility managers but also to customers. It interrupts reliable and continuous power supply to electricity consumers. Especially explosion of transformers in summer seasons of Indian states like Chhatisgargh, Rajasthan, Tamilnadu, Telangana etc. is a huge concern to the authorities of electricity transmission and distribution. Transformers operating in hot climatic conditions are often heavily loaded, especially in peak summer months when electricity demand for cooling rises. High ambient temperatures, heavy electrical loading and poor maintenance of transformers increase the risk of severe faults and failures. Ambient temperatures often exceed 45oC in summer. These high temperatures raise additional stresses on transformers, and further accelerate the aging of insulation materials and create hotspots. Combined with the ambient heat, the internal temperature of the transformer rises significantly. As transformer oil heats up, gases such as hydrogen, methane, and acetylene can dissolve in the oil, which can eventually form gas pockets. In severe cases, these gas pockets can ignite and cause an explosion. Regular dissolved gas analysis (DGA) is often underutilized, meaning developing issues may go undetected until they become critical. Also it leads to reduced dielectric strength. This degradation can result in insulation breakdown. Therefore, a great attention is needed to address these significant issues. The proposed research project aims to develop a proto-type for transformers comprising all real-time operating conditions that include peak load, ambient temperatures in extreme summer heat etc. It also comprises an effective condition monitoring mechanism with all necessary measurements. Further it provides the precautionary measures to avoid transformer explosions during extreme hot climates in summer seasons of India. Data collected through various sensors connected to the designed prototype is analyzed using the advanced artificial intelligence driven monitoring network. By processing visual and environmental data, AI can identify overheating and other critical issues, allowing for timely interventions. Researchers and diagnostic experts of transformers generally follow the traditional offline condition monitoring methods to diagnose the faults. Whereas the proposed research shall monitor the transformers in real-time and diagnose the fault before it damages the equipment, using the advanced artificial intelligence technology. This approach not only enhances safety and reliability in power distribution but also reduces the risk of catastrophic failures, ultimately improving infrastructure resilience in harsh climate conditions.