Diabetes presents a significant health challenge worldwide, with early detection playing a crucial role in improving patient outcomes and reducing healthcare costs. Machine learning has emerged as a transformative tool in healthcare, particularly in accelerating diagnoses and enhancing patient care. However, the effectiveness of machine learning models heavily depends on proper hyperparameter tuning, which can significantly influence their performance. This research investigates the impact of various hyperparameter tuning algorithms on machine learning models for the early detection of diabetes, using publicly available UCI Diabetes Dataset. By evaluating models based on accuracy, precision, recall, and F1 score, this study provides valuable insights to help healthcare professionals and data scientists select the most effective hyperparameter tuning methods, ultimately improving the accuracy and efficiency of diabetes diagnosis models.