Bangladesh is a disaster-prone area due to its geographic location, especially since it is affected by a tropical cyclone (TC) almost every year. TC causes severe damage to lives and livelihoods in this region of Bangladesh. TC prediction and monitoring are still based on the traditional statistical model. In general, the conventional statistical model has the limitation of not handling nonlinear datasets in a precious way. However, the country is gradually adopting modern technologies like artificial intelligence (AI), machine learning (ML), and Fourth Industrial Revolution (IR4) technology for disaster management. The purpose of this study is to identify the scope of adopting new technologies like machine learning and deep learning (DL) for cyclone prediction in countries like Bangladesh, which are cyclone-prone but have constraints on funds to invest in this field. To establish the idea, we examine the research work on the TC forecasting model used in the country from 2010 to 2022. This paper examines the TC forecasting model used to identify the scope of improvement in the current system based on AI and process a better cyclone prediction system using an AI-based model. This study intends to reveal the gaps in mainstream cyclone prediction methods and focus on cyclone prediction system improvement. Moreover, this work will summarize the current state of the TC prediction forecasting system in Bangladesh and how the incorporation of modern technology can increase its efficiency. Finally, as a final note, we conclude this paper with the answer of proximity to the proposal of including AI in cyclone detection and prediction systems. A workflow diagram to address cyclone prediction based on ML and DL has also been presented in this paper, which may augment the capacity of the Bangladesh Meteorological Department (BMD) in performing their responsibility. Moreover, some specific recommendations have been proposed to improve the cyclone prediction system in Bangladesh.
