Localization is a topic widely discussed in the field of networking. It can be classified into indoor and outdoor localization, with indoor localization being particularly significant for the development of smart cities. Unlike outdoor localization, which commonly relies on GPS, indoor localization faces challenges due to obstacles such as walls. Consequently, there is a need for a novel indoor localization model, which forms the main focus of our research. The objective of this study was to apply an accurate deep machine learning hybrid model for predicting smartphone locations in indoor environments. To accomplish this, we conducted a comparative analysis of performance accuracy among machine learning algorithms (SVM and KNN) and recurrent neural network algorithms (Bi LSTM and LSTM). Our findings revealed that SVM consistently outperformed KNN across all smartphones, while Bi LSTM exhibited superior performance compared to LSTM. Building upon these results, we devised a collaborative multi-stage structure for our hybrid model, combining the strengths of Bi LSTM and SVM, resulting in the Bi LSTM-SVM model. For our study, we gathered a dataset by utilizing diverse smartphones and access point models. These smartphones collected received signal strength from the access points, which was then adjusted to create an indoor localization dataset. The dataset has passed different preprocessing steps to ensure its compatibility with the models employed. We evaluated the performance of five models: SVM, KNN, Bi LSTM, LSTM, and our proposed Bi LSTM-SVM hybrid model, with our proposed model demonstrating superior performance. Furthermore, we investigated the impact of human presence on indoor localization accuracy, as well as the relationship between the number of access points and localization accuracy. Each smartphone exhibited varying levels of accuracy across the five models. To reconcile these differences, we employed calibration techniques. Consequently, our proposed hybrid model, Bi LSTM-SVM, achieved an accuracy of 89.11% in scenarios without human presence and 78.7% in scenarios with human presence. Keywords: Localization, Indoor Localization, Outdoor Localization, Hybrid Model, Receiving Signal Strength