The world of education must continuously adapt to technological developments to increase learning quality, notably in the use of information and communication technologies in the learning process. In today's world, where everything is becoming increasingly interconnected and globalized, speaking more than one language is an increasingly important talent. It has been suggested that the Linguistic Landscape (LL) method is a valuable instrument for teaching and learning languages. A country's linguistic landscape refers to the visibly apparent languages in public locations, such as signs, advertisements, billboards, and posters. It is a reflection of the linguistic diversity and multilingualism of a community. In recent years, the linguistic landscape has received considerable attention in the field of language education since it has been acknowledged as a rich resource for language learning. This work investigates the influence of the linguistic landscape on language learning and its link with blended learning, learning motivation, and teacher competency. This article intends to study the impact of LL on language acquisition and its link with blended learning, student motivation, and teacher competence. This study intends to investigate how blended learning, learning motivation, and lecturer competency contributes to enhanced learning quality in an Islamic institution, as well as the role of landscape linguistics in improving the interaction between factors. In this 176-participant study, the Confirmatory Factor Analysis (CFA) technique was used, and the Structural Equation Model (SEM) in SmartPLS was used to evaluate the data. The study's findings indicate that all independent and moderating factors impact learning quality. The study suggests that college administrators or instructors enhance the linguistic sign as educational material. The researcher suggests regularly changing the text on the sign to increase the frequency with which pupils are exposed to the target language. Additional research will assess the new LL by incorporating a variable absent from the current investigation.