The computational infrastructure developed in this work combines a computational model of bioheat transfer based on a nonlinear version of the Pennes equation for heterogeneous media with the precise timing and orchestration of the real-time solutions to the problems of calibration, optimal control, data transfer, registration, finite element mesh refinement, cellular damage prediction, and laser control; it is an example of Dynamic-Data-Driven Applications System (DDDAS) in which simulation models interact with measurement devices and assimilates data over a computational grid for the purpose of producing high-fidelity predictions of physical events. The tool controls the thermal source, provides a prediction of the entire outcome of the treatment and, using intra-operative data, updates itself to increase the accuracy of the prediction. A precise mathematical framework for the real-time finite element solution of the problems of calibration, optimal heat source control, and goal-oriented error estimation applied to the equations of bioheat transfer is presented. It is demonstrated that current finite element technology, parallel computer architecture, data transfer infrastructure, and thermal imaging modalities are capable of inducing a precise computer controlled temperature field within a biological domain. The project thus addresses a set of problems falling in the intersection of applied mathematics, imaging physics, computational science, computer science and visualizations, biomedical engineering, and medical science. The work involves contributions in the three component areas of the CAM program; A, Applicable Mathematics; B, Numerical Analysis and Scientific Computing; and C, Mathematical modeling and Applications. The ultimate goal of this research is to provide the medical community a minimally invasive clinical tool that uses predictive computational techniques to provide the optimal hyperthermia laser treatment procedure given real-time, patient specific data.