Pulsed electromagnetic field therapy is increasingly recognized for its capacity to promote tissue healing, reduce inflammation, and minimize pain across a diverse spectrum of health conditions. However, optimizing the therapy's settings, including pulse frequency, current, coil turns, and proximity to the target tissue, presents significant challenges. These arise from the complex nature of human tissue and the dynamic responses to PEMF. To address these challenges, we propose a data-oriented approach that employs advanced machine-learning algorithms to rigorously analyze simulation data and refine the parameters of PEMF therapy. Our strategy involves simulating the interactions between PEMF and biological tissues under various conditions, followed by the application of a Random Forest Regressor model to analyze the resulting data. This process helps determine the most effective combination of parameters, balancing deep tissue penetration against the risk of thermal effects. Our findings indicate that optimal therapeutic outcomes are achieved with lower frequencies and higher currents, coupled with an increased number of coil turns and reduced distance from the tissue. Through detailed visual tools like correlation heat maps, scatter plots, and 3D visualizations, we provide a nuanced understanding of the parameter interplay, guiding the fine-tuning of therapy settings. Our findings therefore have the potential to lead to personalized PEMF therapy protocols tailored to individual patient needs and treatment objectives.