The proliferation of Internet of Things (IoT) devices and embedded systems has necessitated advanced telemetry solutions that can efficiently manage bidirectional data communication while maintaining system reliability and performance. This study investigates the application of machine learning algorithms to optimize microcontroller-based two-way digital telemetry systems, addressing critical challenges in data transmission efficiency, error detection, and predictive maintenance. Through experimental analysis of ARM Cortex-M4 microcontrollers integrated with supervised and unsupervised learning models, this research demonstrates significant improvements in communication reliability, with error rates reduced by 34% and latency decreased by 28% compared to conventional telemetry approaches. The study employed a mixed-methods approach, combining quantitative performance metrics with qualitative assessment of implementation feasibility across various industrial applications. Results indicate that Random Forest and Long Short-Term Memory (LSTM) networks exhibit superior performance in predicting transmission failures and optimizing data routing protocols. The findings contribute to the growing body of knowledge on intelligent embedded systems and provide practical frameworks for implementing ML-enhanced telemetryinresource-constrained environments. This research holds particular significance for aerospace, automotive, and industrial automation sectors where reliable bidirectional communication is missioncritical.