The rapid evolution of Web 4.0, which is fundamentally based on a decentralized, AI-powered internet, demands governance frameworks that harmonize innovation with ethical imperatives. Current solutions for privacy preservation and algorithmic fairness in decentralized ecosystems remain fragmented, struggling to balance regulatory compliance (GDPR, EU AI Act) with technical scalability and bias resilience. In this paper we present the Omni-Governance Framework (OGF), a tiered architecture designed to address three key challenges: (1) the tradeoff between privacy and fairness in federated learning, (2) regulatory inconsistencies across jurisdictions, and (3) the growing vulnerability of classical encryption in a post-quantum world. The OGF architecture integrates three interconnected tiers: the first tier (T1) enforces GDPR compliance through Rényi Differential Privacy (RDP) and a scalable blockchain-based consent mechanism that mathematically links privacy budgets (ϵ) to fairness degradation (ΔF) to measure how much privacy is preserved (ϵ) with how much fairness might be affected (ΔF), helping to strike a thoughtful balance between the two; the second tier (T2) mitigates bias through fairness-constrained synthetic data generation and edge AI deployment, reducing latency by optimizing computational costs = ()/ ; and finally, the third tier (T3) introduces quantum-safe governance with latticebased homomorphic encryption Enc(∇) = + mod and Decentralized Autonomous Ethics Committees (DAECs) that use quadratic voting Vote Cost = ∑ !" to democratize decision-making. OGF’s cross-tier synergies resolve critical gaps in existing frameworks like FATE and PySyft, for example, synthetic data from Tier 2 enhances federated dataset diversity in Tier 1, while neuromorphic auditors in Tier 3 detect bias drift in real time via spiking neural networks # = $ % ∑% &'$ ∑( !'$ !()). The modular structure of the proposed framework enables incremental adoption, depending on an organization's readiness and goals, in line with regulatory maturity, in addition, its quantumresistant design prevents emerging threats to decentralized AI. While challenges remain, such as a 15% increase in latency at Tier 3 due to encryption overhead and ethical concerns associated with tokenized redress, OGF offers a unified framework to bridge the gap between current regulatory requirements with future technological limitations. This work presents a scalable blueprint for ethical AI governance in the Web 4.0 era, ensuring privacy preservation, bias mitigation, and democratized accountability.