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Integrating noisy, sparse, and heterogeneous experimental data with density functional theory (DFT) and machine learning (ML) is essential for reliable alloy design. While DFT enables high-throughput screening, its systematic biases limit predictive accuracy for real engineering alloys. This review synthesizes studies focused on fusing experimental measurements into ML–DFT workflows. We categorize five experimental data types—synthesis conditions, characterization data, property measurements, literature text, and industrial records—and identify six core challenges: noise, sparsity, heterogeneity, bias, missing metadata, and fragmentation. Six key integration strategies are examined: multi-fidelity learning, transfer learning, active learning with experimental feedback, Bayesian uncertainty modeling, multi-task learning, and physics-constrained augmentation. These approaches consistently reduce prediction errors by 30–60% compared to DFT-only models. Seven evidence-based best practices are distilled, emphasizing uncertainty reporting, data harmonization, experimental hold-out validation, and FAIR data sharing. Case studies demonstrate substantial gains in discovery efficiency for high-entropy alloys, superalloys, and phase diagrams. Remaining gaps, particularly the lack of standardized experimental databases and real-time feedback systems, are highlighted. This work provides a practical taxonomy and roadmap for developing experimentally grounded ML models that accelerate the design of high-performance alloys for aerospace, energy, and biomedical applications.