Time series forecasting plays a pivotal role in domains ranging from finance and healthcare to multimodal analytics, yet achieving robust accuracy across heterogeneous datasets and varying prediction horizons remains a significant challenge. In this paper, we propose a novel hybrid forecasting framework that integrates advanced metaheuristic optimization with deep learning architectures for multi-domain, multi-scale prediction. The core of our approach is a dual-stage optimization strategy that combines the global exploration capability of the Aquila Optimizer (AO) with the guided local refinement of the Proposed Guided Remora Optimization Algorithm (PGRO). This hybrid optimization mechanism is applied to fine-tune model hyperparameters, fusion strategies, and incremental learning settings for architectures such as Long Short Term Memory (LSTM), Transformer, and multimodal fusion networks. The framework is further enhanced by an incremental learning module with memory replay, enabling continual adaptation without catastrophic forgetting. We evaluate the proposed approach on 6 diverse datasets (National Association of Securities Dealers Automated Quotations/New York Stock Exchange (NASDAQ/NYSE) Historical Stock, Financial Question Answering (FiQA), Financial PhraseBank, Reddit Financial News, Fourth Makridakis Forecasting Competition Time series (M4), and Video and Text (VaTeX) Multimodel), spanning structured numerical, textual, social media, and multimodal time series. Experimental results show that our AO + PGRO-enhanced models consistently outperform classical statistical models, deep learning baselines, and state-of-the-art transformer-based forecasters in both short-and long-horizon prediction tasks. Ablation studies confirm the contribution of each component, and detailed horizon-wise analysis highlights the method’s adaptability to varying temporal dependencies. The proposed framework delivers state-of-the-art accuracy, stability, and generalization across domains, offering a powerful and extensible approach for real-world time series forecasting challenges.