Recent research in defense mechanisms against data flooding attacks in Mobile Ad Hoc Networks (MANETs) emphasizes the development of intelligent, trust-based, and learning-driven techniques to mitigate the impact of excessive packet generation that leads to bandwidth exhaustion and node resource depletion. Traditional threshold-based methods have evolved into hybrid deep learning frameworks integrating CNN, LSTM, and GRU architectures to accurately detect anomalous traffic patterns in real time. Trust-enhanced routing protocols like TAODV dynamically isolate malicious nodes based on behavioral reputation, improving packet delivery and reducing routing overhead. Moreover, models using chained hash-table filtering and recurrent neural networks (RNNs) effectively prevent propagation of malicious packets with high detection accuracy. Recent advancements also explore lightweight, energy-efficient, and distributed detection schemes suitable for resource-constrained nodes, ensuring reliable communication and robust defense against flooding and denial-of-service attacks in dynamic MANET environments.