An intelligent industrial diagnostic system (IIDS) is presented, specifically designed for the analysis of high-frequency data streams originating from fragmented IIoT infrastructures characterized by polling intervals of 1–60 s and local buffer capacities up to 128 MB per sensor node. The multistage pipeline incorporates data cleansing, imputation, and heterogeneous aggregation, generating feature matrices suitable for algorithmic routing and cluster-adaptive analytics. Dimensionality reduction is performed using PCA, t-SNE, and UMAP, after which latent operational states are identified via structurally adaptive clustering (KMeans, Agglomerative Clustering) with silhouette-based validation and hierarchical dendrograms. The hybrid anomaly detection subsystem combines Isolation Forest, bidirectional CUSUM, threshold-based IQR analysis, and EWMA regression on residuals, thus enabling the detection and classification of rare events under stochastic fluctuations and irregular failures. Diagnostic inferences are routed to the Core Engine, which operates on a situational rule-based logic framework with scenario adaptation support. Visual analytics is implemented through 2D/3D projections, dendrograms, Sankey diagrams, geoinformation maps, and Dash-based panels; the strategic module interprets cluster-derived insights for translation into control recommendations within automated decision-making systems. Validation on synthetic datasets (up to 10,000 records) and industrial archives (up to 125,000 entries) demonstrated anomaly detection accuracy exceeding 90%, with average processing times around 3.5 s per 1,000 rows, confirming the computational robustness of the architecture under stochastic deviations and low-frequency rare failures. The developed system enables the implementation of adaptive diagnostics, predictive maintenance, and digital management in energy, oil and gas, and metallurgical industries, with particular applicability to industrial clusters and distributed infrastructures in BRICS countries.