We present Delta-Adjust, a novel interpolation method that extends local neighborhood interpolation techniques by introducing per-feature neighbor selection and feature-vector difference bias adjustment. Unlike conventional prediction models, Delta-Adjust operates without explicit training phases. It leverages the structure of information entangled within the dataset to interpolate across a domain using small, local adjustments. This mechanism enables Delta-Adjust to infer a valid global representation of data only from local relationships. This formulation applies indifferently to both regression and classification problems, and potentially to broader predictive tasks. Though not a machine learning model in the conventional sense, Delta-Adjust can be used for predictive modeling due to its structure-preserving nature. Our experiments demonstrate strong performance across standard benchmarks (Iris: F1=0.94; Breast Cancer: F1=0.86; California Housing: MSE=0.29), while offering competitive interpolation accuracy across synthetic surfaces with discontinuities, curvature, and high-frequency patterns (e.g. Circular Ridges). As a general-purpose interpolation technique intended for ML applications, Delta-Adjust holds potential as a computational primitive for AI pipelines. Its framework, based on local propagation of deltas, naturally accommodates a wide range of predictive tasks and may support future applications in geospatial modeling, computer graphics, sensor networks, and representation learning. This work proposes a new direction for information-aware computation in data-driven systems—relying on local interpolations that cooperatively induce a global model of feature–output relationships. Delta-Adjust achieves this independently of the underlying data distribution, without any training steps, and without requiring explicit assumptions about global model structure.