This project proposes a non-invasive wearable device for fertility monitoring and effective and flexible statistical learning algorithm to detect and predict ovulation using data captured by this device. The system consists of an earpiece, which measures the ear canal temperature every 5 minutes during night sleep hours, and a base station that transmits data to a smartphone application for analysis. We establish a data cleaning protocol for data preprocessing and then fit a Hidden Markov Model (HMM) with two hidden states of high and low temperature to identify the more probable state of each time point via the predicted probabilities. Finally, a post-processing procedure is developed to incorporate biorhythm information to form a time course biphasic profile for each subject. The performance of the proposed algorithms applied to data collected by the device are compared with traditional methods in terms of match rate with self-reported ovulation days confirmed with an Ovulation Test Kit. Empirical study results from a group of 34 users yielded significant improvements over the traditional methods in terms of detection accuracy (with sensitivity 92.31%) and prediction power (23.07-31.55% higher). We demonstrated the feasibility for reliable ovulation detection and prediction with high frequency temperature data collected by a non-invasive wearable device. Significance: Traditional fertility monitoring methods are often either inaccurate or inconvenient. The wearable device and learning algorithm presented in this project provides a user friendly and reliable platform for tracking ovulation, which may have a broad impact on both fertility research and real world family planning. This project is implemented with MATLAB software.