Many traditional quantum dynamics methods are explored to study the excitation energy transfer in FMO complex. All of them are recursive, i.e., require iterative dynamics propagation and at the same time, computationally expensive. Recently, ML has been successfully applied to accelerate quantum dynamics. ML-based approaches adopted in the literature so far, can be divided into two categories: recursive and non-recursive approaches. ML-based recursive approaches where dynamics at some time t depends on the dynamics of previous time-steps, are quite successful but there are some downsides of them; first, iterative propagation is inherently slow and may lead to error accumulation and second, they need a short-time trajectory generated with traditional quantum dynamics methods to initiate trajectory propagation.