Urban traffic congestion prediction under strict no backpropagation constraints demands a fundamental rethinking of model design. The Barbados Traffic Analysis Challenge forbids gradient based optimization, favour-ing biologically plausible or closed form learning. We explore this setting through a structured progression from video based vehicle detection with MediaPipe and MobileNetv2 ESN, through the Forward Forward algorithm and Extreme Learning Machines, to a final Deep Echo State Network DeepESN architecture. Ratherthan processing the raw 500GB video corpus, we demonstrate with empirical and literature backed evidence that compact tabular metadata from traffic sensors delivers competitive accuracy at a fraction of the compute and storage cost. We present a thorough exploratory data analysis revealing severe class imbalance (62% free-flowing) and high intra-block congestion entropy, a systematic hyperparameter sensitivity study across five Deep-ESN parameters, and a detailed ablation study quantifying the contribution of each feature group. Our final DeepESN, trained analytically via ridge regression, achieves 61.7% validation accuracy and a Macro F1 of 0.584 on the held out set, earning a rank of 80th on the competition's private leaderboard among 1,839 participants and qualifying for a bronze medal. The source code and all experiment artefacts are available athttps://github.com/Samir-atra/Barbados_Traffic_ Analysis_Challenge_dev.