<p>Environmental time series forecasting, like air pollution, dew points, wind, and temperature, has serious challenges attributed to high variance, multicollinearity, and intricate dependencies. Regular models like ARIMA and baseline LSTMs tend to lose their performance based on feature redundancy, computational effectiveness, and model uncertainty estimation. To overcome these constraints, introduce the ICA-LSD Bayesian LSTM, a new hybrid method combining Independent Component Analysis (ICA) for noise reduction in feature extraction, Latin Square Design (LSD) for optimally tuning hyperparameters, and Bayesian Updating for making prediction uncertainty-aware. ICA removes noise by extracting statistically independent components, enhancing the model’s predictive capability. LSD optimally selects hyperparameters through systematic design, outperforming random and grid search methods with much lower computational expense. Bayesian Updating provides improved prediction certainty by including observed information to update forecast distributions dynamically. Our method performs better than state-of-the-art (SOTA) models, such as transformer-based models and deep LSTMs, in terms of Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), R², and Mean Absolute Percentage Error (MAPE), but with higher interpretability and efficiency. While it has its merits, the model imposes linear independence on ICA, which might not always be accurate, and Bayesian inference depends on properly defined priors. Potential future extensions can investigate hybrid models that use transformers and LSTMs improved with ICA for better long-range dependency learning. In all, ICA-LSD Bayesian LSTM offers an efficient, interpretable, and computationally friendly solution to multivariate time series forecasting that is superior to standard deep learning approaches while guaranteeing strong uncertainty estimation.</p>