The substantial energy demands for climatization in residential buildings place this sector at the forefront of global energy consumption challenges, making it a critical focus area for energy efficiency research. Numerous controllable elements within buildings influence indoor environmental quality (IEQ), rendering manual optimization by humans infeasible. Fortunately, the proliferation of IoT devices and smart home technologies unlocked new opportunities for intelligent algorithms that optimize building energy efficiency by IEQ management. Building energy efficiency can be enhanced through intelligent control systems or user feedback, which always requires decision algorithms to feed the control or user alert systems. For real-time prediction of IEQ and energy consumption, these algorithms rely on models of the buildings and their occupancy. Different approaches and methodologies exist to develop these models, with varying computational requirements, scalability potential, and precision. This systematic review, complying with PRISMA2020 guidelines, aims to evaluate the existing literature on algorithms and methodologies to optimize energy consumption and IEQ. A comprehensive search strategy was developed using thematic blocks of keywords to address the questions raised on the scope of this review. These keywords aggregate terms related to residential buildings, IEQ, energy optimization, algorithms and methodologies, control systems, performance evaluation, and all IEQ components. The search was performed within the Scopus database with inclusion criteria limited to peer-reviewed articles published in English. This review analyzes the IEQ management solutions’ type, precision, real-world implementation, and efficiency. It identifies crucial gaps in the current literature, assesses the maturity and limitations of contemporary technologies, and identifies promising areas for future research in smart home energy-efficient solutions.