Traditional grass-cutting equipment whether gasoline-driven internal combustion engine (ICE) mowers or manually operated push mowers — carries a well-documented set of costs: elevated carbon emissions, disruptive noise levels, ongoing fuel expenditure, and considerable physical labor on the part of the operator. In response, the past decade of research has gravitated toward solar photovoltaic (PV) powered robotic mowers as a cleaner alternative. Yet systems built around solar energy alone remain structurally fragile: their performance is dictated by seasonal sunlight variation, cloud cover, shading from surrounding trees or structures, and stalled battery replenishment whenever overcast conditions persist. This review sets out to examine that fragility in depth and to consolidate current progress toward a more resilient alternative — autonomous lawn-care machines that draw on both solar and wind energy simultaneously. Twenty-six studies, spanning foundational designs through the most recent contemporary work, are examined across four dimensions: the embedded microcontroller platforms that govern these robots (Arduino, ATmega, PIC, ESP32), the wireless interfaces used for teleoperation (Bluetooth Classic, BLE, and companion Android applications), the sensing strategies used for navigation and obstacle avoidance (ultrasonic, infrared, and multi-sensor arrays), and the renewable charging topologies and micro-wind rotor designs relevant to a dual-source power system. From this synthesis, the paper distills the recurring shortcomings of existing designs into a structured comparative taxonomy and, building on that analysis, proposes a conceptual architecture for an autonomous grass-cutting machine powered jointly by solar PV and a micro-wind turbine — one incorporating intelligent power arbitration between the two sources, a Bluetooth-based manual override, and a robust multi-sensor obstacle-detection scheme.