Data-driven surrogate models are increasingly used in computational fluid dynamics, and their reliability depends on the quality of the training data. These models are typically trained on fixed, pre-generated datasets. Systematic surrogate studies require controlled data generation, in which datasets can be regenerated, adapted, or extended to match specific research requirements. We introduce ChannelFlow-Tools, an open-source, configuration-driven pipeline for generating ML-ready datasets of three-dimensional obstructed channel flows. The pipeline integrates procedural obstacle geometry generation across six shape families, signed-distance-field (SDF) voxelisation, lattice-Boltzmann simulation, and packaging into ML-ready tensors. The workflow is driven by configuration files, with byte-identical reproducibility verified for the geometry-generation stage. The pipeline is evaluated through a full-corpus mesh-integrity audit, analytical and corpus-level validation of the SDF representation, canonical sphere-flow benchmarks for the solver, and a per-scene data-integrity audit. To demonstrate that the pipeline produces physically consistent and directly usable training data, three surrogate models (3D U-Net, FNO, and U-FNO) are trained on a sample dataset of 450 simulations spanning $Re_c \approx 1000$-$10{,}000$, generated entirely through the pipeline. The models learn the geometry-to-flow mapping and show physically interpretable behaviour on shape-family and Reynolds-number out-of-distribution splits, confirming direct downstream usability. ChannelFlow-Tools thus provides shared, auditable infrastructure for controlled benchmarking of geometry-aware CFD surrogates.

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