The rapid growth of artificial intelligence (AI), machine learning, and heterogeneous data sources has increased the need for scalable data-lake architectures capable of supporting concurrent workloads, diverse data types, and dynamically changing computational requirements. Conventional data-management approaches often struggle to provide adequate isolation, resource elasticity, governance, and intelligent workload coordination in multi-tenant environments. This research proposes a conceptual scalable multi-tenant framework for AI-driven big data lake management and processing in which tenant-aware orchestration, workload classification, resource allocation, data governance, and AI-assisted decision mechanisms operate as integrated architectural components. The framework is theoretically grounded in scalable data-lake orchestration, formal reasoning, explainable decision-making, and resource-aware computational management. Particular emphasis is placed on separating tenant-level policies from shared infrastructure while maintaining efficient utilization of storage and processing resources. The architectural rationale is informed by research on rule-based learning, formal verification, satisfiability solving, explainability, and computational reasoning. The proposed framework further incorporates principles associated with multitenant data-lake orchestration for AI workloads (Goyal, 2025). Analytical findings indicate that a policy-aware, AI-driven orchestration layer can improve workload prioritization, resource utilization, isolation, and operational transparency compared with static allocation models. The study also identifies limitations associated with governance complexity, model dependence, computational overhead, and fairness across tenants. The resulting framework provides a research foundation for scalable, intelligent, and explainable big data lake management.