Modeling complex spatiotemporal systems remains a challenging task due to nonlinear interactions, chaotic behavior, and the frequent lack of complete mechanistic knowledge. Traditional approaches rely on approximate mathematical representations, while purely data-driven models often demand large volumes of high-quality data and struggle to generalize to unseen regimes. In this work, we explore hybrid deep learning approaches that integrate imperfect prior knowledge with observational data to model complex dynamics. We focus on two representative systems—the Lorenz system and the Kuramoto–Sivashinsky (KS) equation—to study how knowledge quality and data availability influence predictive performance. Two architectures are employed: Echo State Networks (ESNs) and Long Short-Term Memory (LSTM) networks, both adapted to incorporate knowledge-based predictions. Our empirical analysis shows that incorporating prior knowledge substantially improves the performance of ESNs, while LSTMs demonstrate strong baseline predictive capability with gains from knowledge integration and large data. The results highlight the importance of balancing knowledge and data in hybrid modeling and provide empirical evidence for designing robust deep learning frameworks for spatiotemporal chaotic systems.