Understanding and mitigating health risks from poor indoor air quality, particularly fine particulate matter (PM2.5), is critical, yet conventional monitoring methods are costly and require skilled operators. Low-cost sensors (LCS) offer an accessible alternative; however, their accuracy under varying environmental conditions remains uncertain. This study evaluates how humidity, temperature, deployment duration, and concentration levels affect the calibration accuracy of low-cost PM2.5 monitors. Nineteen Plantower PMS 3003 sensors deployed in 11 Salt Lake County homes participating in the Green & Healthy Homes Initiative were calibrated before and after residential deployment using a TSI DustTrak aerosol monitor. Linear and Lasso regression analyses were performed to evaluate the influence of environmental factors on calibration parameters. Significant variability was observed in environmental conditions. Higher humidity (p = 0.0197) and longer deployment durations (p = 0.0178) significantly altered calibration slopes, while mean PM2.5 exposure (p = 0.0040) was strongly associated with intercept adjustments. These findings emphasize the need to account for environmental factors in calibration models to improve LCS accuracy and reliability. Environmental conditions significantly impact the performance of low-cost PM2.5 sensors. Modeling these impacts can streamline the calibration process, making it more efficient and cost-effective. Future research should focus on refining calibration models and exploring additional environmental factors to optimize LCS performance.