  Google's BBR (Bottleneck Bandwidth and Round-trip Propagation Time) approach
is used to enhance internet network transmission. It is particularly intended
to efficiently handle enormous amounts of data. Traditional TCP (Transmission
Control Protocol) algorithms confront the most difficulty in calculating the
proper quantity of data to send in order to prevent congestion and bottlenecks.
This wastes bandwidth and causes network delays. BBR addresses this issue by
adaptively assessing the available bandwidth (also known as bottleneck
bandwidth) along the network channel and calculating the round-trip time (RTT)
between the sender and receiver. Although when several flows compete for
bandwidth, BBR may supply more bandwidth to one flow at the expense of another,
resulting in unequal resource distribution. This paper proposes to integrate
machine learning with BBR to enhance fairness in resource allocation. This
novel strategy can improve bandwidth allocation and provide a more equal
distribution of resources among competing flows by using historical BBR data to
train an ML model. Further we also implemented a classifier model that is
graphic neural network in the congestion control method.
