Lakehouse systems unify the strengths of data lakes and data warehouses and are rapidly becoming a dominant architecture for analytic data management. The lakehouse architecture decouples system design into interoperable subsystems—execution engines(e.g., Spark, Trino, Presto) and table formats (e.g., Delta Lake, Iceberg, Hudi)—giving users flexibility to mix and match. However, jointly selecting and configuring these subsystems is hard: subsystem choices and configurations interact in complex ways, and online trial-and-error is costly (or infeasible when migration is required). Although there is extensive work on database tuning, most methods target a single subsystem and thus miss cross-dependencies; many also rely on iterative online tuning that is prohibitively expensive. In this work, we present LakeHelm, a zero-shot lakehouse advisor that jointly recommends an engine–format pair and its configuration without online feedback. LakeHelm uses a dual-gate Mixture-of-Experts model: separate gates specialize in engine and format choices, and experts learn configuration surrogates for each subsystem combination. To enhance generalization, we augment training data with generated SQL templates and synthesized workloads,layered atop collected runs that explore the configuration space. Evaluated across five standard benchmarks (TPC-DS, TPC-H, JOB, SSB, SSB-Flat), LakeHelm delivers competitive execution times—averaging 1.35× speedup over a fixed overall-best lakehouse configuration across a large number of workload variations. It achieves this via zero-shot inference on unseen workloads in seconds, without costly online experimentation.