<p>The world is moving toward communication networks of 6G, so security will become a very essential aspect for the integrity and reliability of the system. Advanced anomaly detection and dropout rate estimation techniques are required due to the exponential rise in network traffic, diversity in applications, and interconnectivity of devices. Most of the anomaly detection models until now are encumbered with the issues of scalability, being adaptable to new types of attacks, and the capability of processing large, dynamic network data efficiently. In order to address these challenges, this paper proposes two new techniques: the Variational Autoencoder and Recurrent Transformer Network for 6G (VARNet-6G) for anomaly detection and the Flamingo-Infused Evaporation Rate Optimizer (FIERO) for dropout rate estimation. VARNet-6G deals with sequential data in an efficient way to achieve robust real-time anomaly detection by combining variational auto-encoders with recurrent transformers. On the other hand, FIERO introduces a new optimization technique inspired by natural phenomena for the estimation of dropout rate, which provides highly accurate network performance estimates and ensures network resilience. The proposed schemes have improved by large margins over the existing models, addressing the limitations of traditional techniques in both anomaly detection and dropout rate estimation. The novelty of this work lies in the hybrid approach in combining deep learning with nature-inspired optimization, which guarantees more accurate, scalable, and adaptive solutions to secure 6G networks.</p>