This article presents an algorithmic framework for real-time condition monitoring and state forecasting using multivariate data demonstrated on thermal imagery data of a ship's engine. The proposed method aims to improve the accuracy, efficiency, and robustness of condition monitoring and state predictions by identifying the most informative sampling locations of high-dimensional datasets and extracting the underlying dynamics of the system. The method is based on a combination of Proper Orthogonal Decomposition (POD), Optimal Sampling Location (OSL), and Dynamic Mode Decomposition (DMD), allowing the identification of key features in the system's behavior and predicting future states. Based on thermal imagery data, it is shown how thermal areas of interest can be classified via POD. By extracting the POD modes of the data, dimensions can be drastically reduced and via OSL, optimal sampling locations are found. In addition, nonlinear kernel-based Support Vector Regression (SVR) is used to build models between the optimal locations, enabling the imputation of erroneous data to improve the overall robustness. To build predictive data-driven models, DMD is applied on the subspace obtained by OSL, which leads to an intensive lower demand of computational resources, making the proposed method real-time applicable. Furthermore, an unsupervised approach for anomaly detection is proposed using OSL. The anomaly detection framework is coupled with the state prediction framework, which extends the capabilities to real-time anomaly predictions. In summary, this study proposes a robust predictive condition monitoring framework for real-time risk assessment.