The field of predictive modeling for sustainable energy and environmental systems is rapidly evolving, with a focus on developing innovative solutions to improve forecasting accuracy and optimize resource management. Recent studies have highlighted the potential of machine learning and deep learning techniques in predicting energy consumption patterns, cyclone trajectories, and indoor environmental quality. These advancements have significant implications for ensuring grid stability, minimizing loss of life and infrastructure damage, and promoting sustainable development. Noteworthy papers in this area include: Solar and Wind Power Forecasting: A Comparative Review of LSTM, Random Forest, and XGBoost Models, which provides a comprehensive overview of machine learning algorithms for renewable energy prediction. AQUAIR: A High-Resolution Indoor Environmental Quality Dataset for Smart Aquaculture Monitoring, which introduces a novel dataset for smart aquaculture monitoring and has the potential to improve forecasting and anomaly-detection tools. Evaluation of Machine and Deep Learning Techniques for Cyclone Trajectory Regression and Status Classification by Time Series Data, which demonstrates the effectiveness of machine learning models in predicting cyclone trajectories and status. Optimizing Indoor Environmental Quality in Smart Buildings Using Deep Learning, which proposes a deep learning-driven approach to proactively manage indoor environmental quality parameters and balance building energy efficiency. Hybrid Deep Learning Modeling Approach to Predict Natural Gas Consumption of Home Subscribers on Limited Data, which highlights the importance of incorporating geographical and climatic factors in predictive modeling for natural gas consumption.