The steelpan, a significant musical instrument invented in the mid-20th century, is a harmonically complex system making fundamental pitch detection a difficult task. Initial experiments using state-of-the-art pitch detection algorithms have shown significant difficulties in detecting the pitch of individual steelpan notes. This paper presents a method of improved pitch detection accuracy by combining music information retrieval techniques with machine learning algorithms. An audio sample set consisting of thousands of steelpan notes from ten different tenor steelpans is used to quantitatively evaluate the proposed methodology. Low-level audio features are extracted from the audio samples and used to train a machine learning algorithm which identifies the salient features for pitch estimation. This method’s performance is measured against the current state-of-the-art pitch detection methods, pYIN (a probabilistic autocorrelation method) and CRéPE (a convolutional neural network-based method), showing improved performance at steelpan pitch detection. The machine learning model developed for this paper using this methodology is focused on the tenor steelpan but is intended to lead to a more generalized model for pitch detection of all types of steelpans.