Conformal prediction guarantees marginal coverage, but a pooled calibration quantile can hide systematic undercoverage across heterogeneous regions of the feature space. We introduce Self-Organized Conformal Prediction (SOCP), a calibration scheme that discovers input-space groups with an unsupervised Self-Organizing Map (SOM) trained without calibration labels. At prediction time, the query's best-matching unit (BMU) draws a calibration buffer from one cell, a fixed grid neighborhood, or a prototype-based enlargement. When fixed neighborhoods are too sparse, Regime 3 adds cells by prototype distance, using a global budget selected from training-cell occupancies and the planned calibration size before any calibration score is observed. The predictor and nonconformity score remain unchanged. Cell-only retrieval has exact cell-conditional validity, and each fixed union of cells has exact retrieved-set validity. Interpreting a neighborhood threshold at its central cell incurs an explicit Kolmogorov-Smirnov (KS) bias term. Across ten regression and classification benchmarks, SOCP reduces the weighted coverage gap relative to pooled split conformal prediction on nine datasets. The mean relative change is $-14.3\%$, at a mean output-size change of $+3.0\%$. Under fixed-neighborhood retrieval, SO composition lowers the ten-seed mean WCovGap in $43$ of the $50$ dataset-score comparisons, while SO-SCP lowers it on average over paired seeds for every dataset at all three tested external partition granularities. These results provide a concise route to group-local calibration without supervised partitions or predictor retraining with a diagnostic toolkit, while keeping the cost and limits of locality explicit.

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