Alzheimer’s disease (AD) is a prevalent neurodegenerative disorder that leads to a range of cognitive complications. Early diagnosis is essential in delaying disease progression, improving quality of life, and reducing treatment costs. Unfortunately, early diagnosis is difficult due to the subtle initial brain changes. Existing diagnostic methods suffer from issues relating to accuracy, accessibility, cost, and invasiveness. This study addresses these limitations by leveraging machine learning (ML) to analyze handwriting data as a non-invasive diagnostic tool. Gaussian Naive Bayes (Gaussian NB), GradientBoosting, Support Vector Machine (SVM), RandomForest, and AdaBoost classifier models were trained and validated using Stratified K-fold cross validation. Models were trained on the DARWIN (Diagnosis AlzheimeR WIth haNdwriting) dataset. Techniques such as hyperparameter tuning, feature selection, and strong regularization were incorporated to limit overfitting and ensure the model’s ability to generalize. Performance metrics were acquired by testing the model with a separate validation set. The Support Vector Machine was the best model, achieving an accuracy of 94.49%, sensitivity of 93.53%, and specificity of 95.49%. My results suggest that utilizing Machine Learning to process handwriting data for Alzheimer’s diagnosis is a promising approach to revolutionize AD diagnosis. Handwriting data is easily obtainable, and the model’s simple online use makes it user-friendly and accessible. These results highlight the potential of handwriting-based ML models as accessible, cost-effective, and accurate diagnostic tools for AD. The model’s performance met or even surpassed similar machine learning works. In conclusion, a machine-learning model trained on handwriting features was developed to diagnose Alzheimer’s disease.