This research develops an integrated system that combines the Internet of Things with machine learning for the purpose of optimizing environmental conditions in Mexican poultry farms. A four-module architecture is implemented: IoT Module for real-time environmental data collection through various sensors (DHT22, MQ-7, MQ-137, MG-811), processing and storage module, multivariable machine learning module, and visualization module. Experimental validation was conducted over 62 days in a commercial poultry farm, continuously monitoring critical variables of temperature, humidity, CO2, and NH3. The data were processed using classification and regression algorithms, including Random Forest, neural networks, and Gradient Boosting, to generate real-time recommendations. Random Forest algorithms showed the best classification performance (68% accuracy), while Gradient Boosting achieved the lowest mean square error in regression (RMSE = 1.32). Through variable importance analysis, it was identified that indoor temperature (37.5%), CO₂ levels (18.3%), and bird age (15.7%) are the most significant variables. Therefore, an Agglomerative Hierarchical Clustering analysis (k = 5) was executed, which allowed categorize 5 specific microenvironments. The system implementation makes predictions about the trend of temperature, humidity, NH3, and CO2. The developed system establishes a significant evidence-based advancement for poultry farming in Mexico.