## 1 Introduction  
Bone drilling is an essential procedure in orthopedic surgery, dental implant placement, and other medical interventions that involve the insertion of screws, plates, or prostheses into the bone [1]. However, one of the significant challenges with bone drilling is the generation of excessive heat, which can lead to a phenomenon called thermal osteonecrosis. Thermal osteonecrosis refers to the death of bone cells (osteocytes) due to elevated temperatures [2, 3]. The consequences of thermal osteonecrosis are far-reaching. The affected bone areas may become weakened, leading to poor implant integration, prolonged healing times, or increased susceptibility to post-operative infections [4]. For surgeries that demand robust bone healing, like fracture repairs or spinal fusions, thermal osteonecrosis can even lead to treatment failure [5, 6]. Thermal osteonecrosis can be managed by optimizing the operation of the surgical drilling system [7]. Achieving this often necessitates precise predictions,such as estimating the bone drilling temperature based on the optimal combination of the drilling parameters. The field of machine learning provides efficient techniques for the prediction of bone drilling temperatures based on data [8]. As a branch of computer science, machine learning focuses on how computers can learn autonomously without being directly programmed with specific rules. It encompasses two primary areas: supervised learning and unsupervised learning [9; 10]. In supervised learning, predictive models are developed using labeled data, involving tasks like predicting drilling temperatures through regression. Conversely, unsupervised learning deals with unlabeled data and is often utilized for tasks such as outlier detection through clustering [11]. Machine learning techniques are increasingly employed in the modern Industry 4.0 to forecast uncertainty and manage vast amounts of data. [PERSON] and [PERSON] [12] used machine learning models to alleviate drilling-induced damage to bovine bone. [PERSON] et al. [13] utilized machine learning methods to evaluate the bone characteristics and categorize different specimens. Support vector machine (SVM) techniques have demonstrated significant success in characterizing the rigidity of trabecular bone samples. [PERSON] et al. [14] implemented machine learning models to improve the anticipation of postsurgical facial profiles using video imaging. [PERSON] et al. [15] conducted a comparison of various machine learning models to predict the mechanical strength of orthopedic bone screws, while [PERSON] et al. [16] confirmed different assumptions regarding the use of machine learning models in forecasting thermal injuries. [PERSON] and [PERSON] [17] employed machine learning models for robotic bone drilling, noting the effectiveness of closed-loop signals in detecting drill bit damage and the morphology during sheep femur bone drilling. The aim of developing predictive models is to create models that can accurately predict new data that are not available during their creation [18]. A model trained on a specific dataset cannot be evaluated on the same dataset because it retains a \"memory\" of it. Therefore, the entire dataset is typically split into two parts: one for the training of the model, known as the training set, and the other for the evaluation of the model, known as the test set. This approach allows for the estimation of the model's generalization capabilities, indicating how well it will perform on unseen data. It is crucial to ensure that no information from the test set leaks into the training set when developing predictive models. Models built with training sets containing test set information will produce overly optimistic results. The experiment was designed and optimized using the Response Surface Methodology (RSM) technique. The collected data were analyzed to develop two predictive models for the temperature increase using the support vector machine (SVM) and random forest (RF) methods.