Cloud computing forms the basis for the emerging technologies in various fields, providing a reliable framework for managing resources to meet the needs of different applications. The rapidly increasing energy requirements inherent to cloud computing pose a real problem concerning sustainability. Energy efficiency, fair resource sharing, and performance consistent across the dynamic and heterogeneous cloud computing system are essential since existing approaches introduce inefficiency, energy consumption, and unfair distribution of loads. This research introduces Adaptive Osprey-Bowerbird Optimized Green Cloud Computing with Randomized Attention Coupled Fair Resource Distribution in Scalable Systems (AO-BO-RNCN-MAN) to address these challenges. The proposed framework integrates the Randomized Neural Coupling Network to learn diverse data representations, with the Multi-instance Attention Network to prioritize tasks, and Adaptive Osprey-Bowerbird Optimization, which is a combination of the Osprey Adaptive Algorithm and the Adaptive Bowerbird Optimization for further fine-tuning of the system. By optimizing the placement of virtual machines and scheduling of tasks, the proposed framework guarantees fairness and high utilization of energy with low turnaround time. Performance assessments indicate that the proposed framework outperforms the existing systems with energy efficiency of 99.82%, precise task scheduling of 99.61% and fair resource allocation of 99.74%. AO-BO-RNCN-MAN not only proposes a new way of addressing green computing challenges but also opens the gates to sustainable, adaptive, and scalable designed cloud infrastructures for resource management in cloud ecosystems and establishes the proposed conceptual framework as a new standard.