Location estimation is a very important task in wireless communication systems. Its goal is to determine the geographic position and, in some cases, velocity and direction of motion of wireless devices. The main challenge and critical requirement is to design an accurate and low-cost location system. This technology is utilized for global position system (GPS) and some localization applications in cellular and sensor networks. We propose maximum likelihood (ML) methods for location estimation using received-signal-strength (RSS) and time-of-arrival (TOA) measurements. In the RSS case, we utilize both spatial and temporal measurements where the multipath fading effects are modeled using a Nakagami-[gamma] fading model. We also proposed to model the TOA measurement errors using the gamma distribution characteristic. We also derive Cramér-Rao bound (CRB) for the estimated location under both measurement scenarios. Simulation results show that the proposed algorithms outperform the commonly-used least-squares methods.