Code comments are important in software development because they directly influence software maintainability and overall quality. Bad practices of code comments lead to code comment smells, negatively impacting software maintenance. Recent research has been conducted on classifying inline code comment smells, yet automatically detecting these still remains a challenge. We aim to automatically detect and classify inline code comment smells through machine learning (ML) models and a large language model (LLM) to determine how accurately each smell type can be detected. We enhanced a previously labeled dataset, where comments are labeled according to a determined taxonomy, by augmenting it with additional code segments and their associated comments. GPT 4, a large language model, was used to classify code comment smells on both the original and augmented datasets to evaluate its performance. In parallel, we trained and tested seven different machine learning algorithms on the augmented dataset to compare their classification performance against GPT 4. The performance of models, particularly Random Forest, which achieved an overall accuracy of 69 percent, along with Gradient Boosting and Logistic Regression, each achieving 66 percent and 65 percent, respectively, establishes a solid baseline for future research in this domain. The Random Forest model outperformed all other ML models, by achieving the highest Matthews Correlation Coefficient (MCC) score of 0.44. The augmented dataset improved the overall classification accuracy of the GPT 4 model predictions from 34 percent to 55 percent. This study contributes to software maintainability by exploring the automatic detection and classification of inline code comment smells. We have made our augmented dataset and code artifacts available online, offering a valuable resource for developing automated comment smell detection tools.