  This study suggests a new strategy for improving congestion control by
deploying Long Short-Term Memory (LSTM) networks. LSTMs are recurrent neural
networks (RNN), that excel at capturing temporal relationships and patterns in
data. IoT-specific data such as network traffic patterns, device interactions,
and congestion occurrences are gathered and analyzed. The gathered data is used
to create and train an LSTM network architecture specific to the IoT
environment. Then, the LSTM model's predictive skills are incorporated into the
congestion control methods. This work intends to optimize congestion management
methods using LSTM networks, which results in increased user satisfaction and
dependable IoT connectivity. Utilizing metrics like throughput, latency, packet
loss, and user satisfaction, the success of the suggested strategy is
evaluated. Evaluation of performance includes rigorous testing and comparison
to conventional congestion control methods.
