The health of bee colonies is vital for ecosystem sustainability and agricultural productivity due to bees' critical role in pollination. Traditional monitoring methods are often invasive and labor-intensive, lacking the ability to detect early signs of colony distress. This research proposes the utilization of advanced LSTM neural network models to analyze acoustic bioindicators as a non-invasive approach for monitoring bee colony health. We employ a meticulously curated dataset comprising monthly audio recordings from various bee colonies across different countries, enriched with metadata such as colony number and country of origin. The dataset also includes comprehensive biological data on pathogens, hormones, metabolic indicators, and parasites, featuring specific markers like ABPV, BQCV, DWV, IABPV, KBV, LSV1-4, P.larvae, SBV, V.apis, and V.ceranae. Leveraging the temporal sequence modeling capabilities of LSTMs, the proposed model is designed to capture intricate patterns within the audio data that correlate with changes in colony health. The LSTM neural network's proficiency in handling sequential data makes it particularly suited for analyzing the dynamic acoustic signals produced by bee colonies, which may contain subtle indicators of stress or disease not detectable through conventional analysis. The anticipated outcome is a predictive model capable of identifying early signs of unhealthy conditions within bee colonies. This early detection system would enable beekeepers to implement preventive measures promptly, potentially averting colony collapse and supporting sustainable apicultural practices. By providing a real-time, non-invasive monitoring tool, this approach could significantly enhance bee health management, with positive implications for agricultural output and ecological balance. This research aims to bridge the gap between advanced machine learning techniques and practical apicultural applications, demonstrating the potential of combining acoustic analysis with LSTM neural networks to monitor and preserve bee colony health effectively.