We investigate whether hate-speech intensity on public Telegram channels serves as a conflict signal, using 1,514 days of Arabic-language posts from Sudan aligned with ACLED conflict-event records. Applying a pretrained multilingual hate-speech classifier, we construct a daily intensity index and analyze its relationship to political violence using within-regime analysis and autocorrelation-robust moving-block bootstrap inference. Hate-speech intensity increases approximately fourfold over the months following war onset and remains elevated, but day-to-day fluctuations do not predict short-term changes in violence. Within the post-war period, the index is positively associated with explosions and remote violence (r=+0.37) and negatively associated with protests (r=-0.25) and riots (r=-0.30), with all three associations surviving multiple-comparison correction. These patterns also hold for continuous classifier-score measures, although the regime-level increase is closely intertwined with growth in posting volume and active-channel participation. We further show that pooling across conflict regimes inflates effect sizes, while naive inference on serially dependent time series overstates statistical significance. Hate-speech intensity should therefore be interpreted as a descriptive marker of the conflict-related Telegram environment rather than as an early-warning predictor of imminent violence.